Communication-Efficient Learning of Deep Networks from Decentralized Data

Abstract

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.

[v4] Fixes a typo in the FedAvg pseudocode. [v3] Updates the large-scale LSTM experiments, along with other minor changes

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• Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
• A Federated Filtering Framework for Internet of Medical Things
• Secure Social Recommendation based on Secret Sharing
• Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
• Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
• Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
• HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
• LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
• FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
• Data-driven geophysics: from dictionary learning to deep learning
• Exploiting Unlabeled Data in Smart Cities using Federated Learning
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
• Communication-Efficient Federated Distillation
• Federated Heavy Hitters Discovery with Differential Privacy
• Ternary Compression for Communication-Efficient Federated Learning
• Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
• Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
• ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks
• Federated Learning for Malware Detection in IoT Devices
• Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
• Robustifying Models Against Adversarial Attacks by Langevin Dynamics
• A Novel Attribute Reconstruction Attack in Federated Learning
• Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
• Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
• Byzantine-Robust and Privacy-Preserving Framework for FedML
• Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
• Distributed stochastic optimization with gradient tracking over strongly-connected networks
• Achieving Model Fairness in Vertical Federated Learning
• p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks
• IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
• Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
• Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency
• Local SGD With a Communication Overhead Depending Only on the Number of Workers
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices
• Turn Signal Prediction: A Federated Learning Case Study
• Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
• Gradient Descent with Compressed Iterates
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
• FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
• Optimal query complexity for private sequential learning against eavesdropping
• Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression
• JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training
• Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
• FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning
• Machine Learning in/for Blockchain: Future and Challenges
• Optimized Power Control Design for Over-the-Air Federated Edge Learning
• Free-rider Attacks on Model Aggregation in Federated Learning
• Quantized Densely Connected U-Nets for Efficient Landmark Localization
• LOGAN: Membership Inference Attacks Against Generative Models
• Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
• FedNL: Making Newton-Type Methods Applicable to Federated Learning
• Deep Learning for Face Anti-Spoofing: A Survey
• rTop-k: A Statistical Estimation Approach to Distributed SGD
• FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
• Information Leakage in Embedding Models
• Privacy Threats Against Federated Matrix Factorization
• SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
• What Do We Mean by Generalization in Federated Learning?
• Layer-wise Characterization of Latent Information Leakage in Federated Learning
• Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning
• Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective
• Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
• Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?
• Efficient and Private Federated Learning with Partially Trainable Networks
• STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
• FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching
• PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference
• Bandit Online Learning with Unknown Delays
• Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection
• Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
• Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
• Privacy-Preserving News Recommendation Model Learning
• Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
• Efficient Sparse Secure Aggregation for Federated Learning
• Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning
• An introduction to decentralized stochastic optimization with gradient tracking
• Sustainable Federated Learning
• Accelerating Federated Learning via Momentum Gradient Descent
• FedGL: Federated Graph Learning Framework with Global Self-Supervision
• Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
• Personalized Federated Learning with Gaussian Processes
• Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
• Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing
• On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times
• 2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments
• Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
• Local SGD: Unified Theory and New Efficient Methods
• FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
• Defending against Reconstruction Attack in Vertical Federated Learning
• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
• Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles
• Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
• On Tilted Losses in Machine Learning: Theory and Applications
• DRIVE: One-bit Distributed Mean Estimation
• Faster Non-Convex Federated Learning via Global and Local Momentum
• FedSiam: Towards Adaptive Federated Semi-Supervised Learning
• BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture
• Coded Computing for Federated Learning at the Edge
• Byzantine-Resilient Secure Federated Learning
• On the relationship between (secure) multi-party computation and (secure) federated learning
• Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
• FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
• From Local SGD to Local Fixed-Point Methods for Federated Learning
• Collaborative Fairness in Federated Learning
• From Federated Learning to Federated Neural Architecture Search: A Survey
• One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
• Key Protected Classification for Collaborative Learning
• Accelerating DNN Training in Wireless Federated Edge Learning Systems
• Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
• A Marauder's Map of Security and Privacy in Machine Learning
• Learning Rate Adaptation for Federated and Differentially Private Learning
• Weighted Distributed Differential Privacy ERM: Convex and Non-convex
• Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy
• From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems
• Federated Semi-Supervised Learning with Class Distribution Mismatch
• Revealing and Protecting Labels in Distributed Training
• Federated Mixture of Experts
• Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
• Hybrid Federated Learning: Algorithms and Implementation
• Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning
• Evaluation Framework For Large-scale Federated Learning
• Learning Discrete Distributions from Untrusted Batches
• Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution
• DistFL: Distribution-aware Federated Learning for Mobile Scenarios
• Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning
• Federated Dynamic Spectrum Access
• Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
• Federated learning with class imbalance reduction
• Federated $f$-Differential Privacy
• Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
• Efficient Algorithms for Federated Saddle Point Optimization
• The More, the Better? A Study on Collaborative Machine Learning for DGA Detection
• RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data
• Toward an Automated Auction Framework for Wireless Federated Learning Services Market
• Anonymizing Data for Privacy-Preserving Federated Learning
• Improving Accuracy of Federated Learning in Non-IID Settings
• Fast-Convergent Federated Learning with Adaptive Weighting
• Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
• Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
• Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
• Federated Noisy Client Learning
• FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
• Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
• FedDANE: A Federated Newton-Type Method
• Towards Federated Bayesian Network Structure Learning with Continuous Optimization
• Distributed Optimization over Block-Cyclic Data
• A Federated Semi-Supervised Learning Approach for Network Traffic Classification
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning
• Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
• Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
• FedE: Embedding Knowledge Graphs in Federated Setting
• Wireless for Machine Learning
• Communication-efficient SGD: From Local SGD to One-Shot Averaging
• Privacy-Preserving Distributed Learning Framework for 6G Telecom Ecosystems
• Concentrated Differentially Private and Utility Preserving Federated Learning
• Distributed Deep Learning with Event-Triggered Communication
• Unified Group Fairness on Federated Learning
• Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach
• Federated Learning on Non-IID Data: A Survey
• Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
• Trustworthy AI in the Age of Pervasive Computing and Big Data
• Auction Based Clustered Federated Learning in Mobile Edge Computing System
• FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
• Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees
• Federated Transfer Learning with Dynamic Gradient Aggregation
• Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
• Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
• Secure Byzantine-Robust Distributed Learning via Clustering
• Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework
• Towards Fair Federated Learning with Zero-Shot Data Augmentation
• Federated Learning in ASR: Not as Easy as You Think
• Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
• Two-stage Federated Phenotyping and Patient Representation Learning
• Practical One-Shot Federated Learning for Cross-Silo Setting
• Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy
• Gradient tracking and variance reduction for decentralized optimization and machine learning
• Fairness-aware Agnostic Federated Learning
• RelaySum for Decentralized Deep Learning on Heterogeneous Data
• Clustering with Distributed Data
• Model Fusion via Optimal Transport
• Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
• Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data
• Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning
• Towards Practical Few-shot Federated NLP
• SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
• FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT
• Differentially Private ADMM for Convex Distributed Learning: Improved Accuracy via Multi-Step Approximation
• Benchmarking Differential Privacy and Federated Learning for BERT Models
• FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
• Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
• Privacy-preserving Decentralized Aggregation for Federated Learning
• Mitigating Sybil Attacks on Differential Privacy based Federated Learning
• Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis
• Anarchic Federated Learning
• A Primal-Dual Algorithm for Hybrid Federated Learning
• Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
• CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression
• Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference
• Cost-Effective Federated Learning in Mobile Edge Networks
• COKE: Communication-Censored Decentralized Kernel Learning
• Voting-based Approaches For Differentially Private Federated Learning
• A Survey on Fault-tolerance in Distributed Optimization and Machine Learning
• ESMFL: Efficient and Secure Models for Federated Learning
• Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction
• A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
• Communication-Efficient Distributed SVD via Local Power Iterations
• Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models
• Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms
• OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
• Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
• Constrained Generative Adversarial Network Ensembles for Sharable Synthetic Data Generation
• GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
• Cloud-based Federated Boosting for Mobile Crowdsensing
• Distributed Computation for Marginal Likelihood based Model Choice
• Network-Aware Optimization of Distributed Learning for Fog Computing
• Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
• Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
• Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
• Fast Convergence Algorithm for Analog Federated Learning
• Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
• Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
• FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning
• Federated Multi-Armed Bandits
• Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
• On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
• Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation
• Byzantine-resilient Decentralized Stochastic Gradient Descent
• Resource-Constrained Federated Learning with Heterogeneous Labels and Models
• Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning
• Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning
• Fast Analog Transmission for High-Mobility Wireless Data Acquisition in Edge Learning
• Improved Differentially Private Decentralized Source Separation for fMRI Data
• QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
• AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization
• Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent
• Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
• Prospects of federated machine learning in fluid dynamics
• Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
• A General Method for Robust Learning from Batches
• How Fine-Tuning Allows for Effective Meta-Learning
• Federated Nonconvex Sparse Learning
• Federated Face Presentation Attack Detection
• Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
• Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks
• Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift
• Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer
• Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
• IFedAvg: Interpretable Data-Interoperability for Federated Learning
• Federated Learning in Adversarial Settings
• On Consensus-Optimality Trade-offs in Collaborative Deep Learning
• Federated Action Recognition on Heterogeneous Embedded Devices
• Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation
• Federated Learning of User Authentication Models
• On the Communication Latency of Wireless Decentralized Learning
• FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning
• Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
• Addressing Class Imbalance in Federated Learning
• Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing
• FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data
• CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data
• Generation of Synthetic Electronic Health Records Using a Federated GAN
• On the Convergence of Decentralized Adaptive Gradient Methods
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients
• Composable Sketches for Functions of Frequencies: Beyond the Worst Case
• Vertical federated learning based on DFP and BFGS
• Proximal and Federated Random Reshuffling
• FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic
• Decentralised Person Re-Identification with Selective Knowledge Aggregation
• Accuracy and Privacy Evaluations of Collaborative Data Analysis
• Personalised Federated Learning: A Combinational Approach
• Trends and Advancements in Deep Neural Network Communication
• Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring
• OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
• Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
• A Taxonomy Study on Securing Blockchain-based Industrial Applications: An Overview, Application Perspectives, Requirements, Attacks, Countermeasures, and Open Issues
• Distributed generation of privacy preserving data with user customization
• Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening
• Data collaboration analysis for distributed datasets
• Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
• LocalNewton: Reducing Communication Bottleneck for Distributed Learning
• On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps
• Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
• Distributing Intelligence to the Edge and Beyond
• Policy-Based Federated Learning
• Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication
• Personalized Federated Learning of Driver Prediction Models for Autonomous Driving
• Stochastic Distributed Optimization for Machine Learning from Decentralized Features
• A Survey of Mobile Computing for the Visually Impaired
• Faithful Edge Federated Learning: Scalability and Privacy
• Distributed Deep Learning in Open Collaborations
• Source Inference Attacks in Federated Learning
• Robust Federated Learning with Attack-Adaptive Aggregation
• Decentralized Differentially Private Segmentation with PATE
• Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
• Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
• Efficient and Less Centralized Federated Learning
• Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
• Federated Learning for Coalition Operations
• Ensemble Federated Adversarial Training with Non-IID data
• SLSGD: Secure and Efficient Distributed On-device Machine Learning
• PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
• Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device
• Training Neural Networks with Fixed Sparse Masks
• Wireless MapReduce Distributed Computing
• R-GAP: Recursive Gradient Attack on Privacy
• Private Language Model Adaptation for Speech Recognition
• Server Averaging for Federated Learning
• MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks
• Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases
• Communication Efficient Federated Learning with Energy Awareness over Wireless Networks
• Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
• Towards Model Agnostic Federated Learning Using Knowledge Distillation
• Learning Federated Representations and Recommendations with Limited Negatives
• FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
• FedGES: A Federated Learning Approach for BN Structure Learning
• Learning to Recommend via Meta Parameter Partition
• A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!
• WAFFLE: Watermarking in Federated Learning
• HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
• High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
• FedSC: Federated Learning with Semantic-Aware Collaboration
• Data Selection for Efficient Model Update in Federated Learning
• FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
• Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
• Cost-Effective Federated Learning Design
• AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning
• A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
• Federated Acoustic Modeling For Automatic Speech Recognition
• Federated Generative Adversarial Learning
• Communication-Efficient Agnostic Federated Averaging
• State-of-the-art Techniques in Deep Edge Intelligence
• MeDaS: An open-source platform as service to help break the walls between medicine and informatics
• Federated Learning with Fair Averaging
• Robust Coreset Construction for Distributed Machine Learning
• Incremental Without Replacement Sampling in Nonconvex Optimization
• Performance Analysis and Characterization of Training Deep Learning Models on Mobile Devices
• FedOCR: Communication-Efficient Federated Learning for Scene Text Recognition
• One-Shot Federated Learning with Neuromorphic Processors
• Federated CycleGAN for Privacy-Preserving Image-to-Image Translation
• Multi-Level Local SGD for Heterogeneous Hierarchical Networks
• Federated Functional Gradient Boosting
• Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity
• Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning
• DP-REC: Private & Communication-Efficient Federated Learning
• Assisted Learning for Organizations with Limited Imbalanced Data
• Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates
• Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts
• FedSmart: An Auto Updating Federated Learning Optimization Mechanism
• Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt
• Coded Computing for Master-Aided Distributed Computing Systems
• Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning
• Gradient Inversion with Generative Image Prior
• Optimal Robust Learning of Discrete Distributions from Batches
• FedPrune: Towards Inclusive Federated Learning
• Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
• Over-the-Air Federated Learning with Retransmissions (Extended Version)
• Federated Natural Language Generation for Personalized Dialogue System
• Exploiting Heterogeneity in Robust Federated Best-Arm Identification
• Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges
• FAT: Federated Adversarial Training
• LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
• Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis
• One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
• Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
• Federated Unlearning
• Federated Multi-task Hierarchical Attention Model for Sensor Analytics
• 1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
• Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection
• Accelerating Gossip SGD with Periodic Global Averaging
• Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
• Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
• Federated Learning From Big Data Over Networks
• Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints
• Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
• Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning
• Convergence Analysis and System Design for Federated Learning over Wireless Networks
• Collaborative Deep Learning Across Multiple Data Centers
• Critical Learning Periods in Federated Learning
• FedGraph: Federated Graph Learning with Intelligent Sampling
• Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
• On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective
• Distributed Learning for Melanoma Classification using Personal Health Train
• Linear mixed modelling of federated data when only the mean, covariance, and sample size are available
• Adaptive Distillation for Decentralized Learning from Heterogeneous Clients
• Accelerating MoE Model Inference with Expert Sharding
• FedDropoutAvg: Generalizable federated learning for histopathology image classification
• Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications
• FLRA: A Reference Architecture for Federated Learning Systems
• Boosting Asynchronous Decentralized Learning with Model Fragmentation
• More Industry-friendly: Federated Learning with High Efficient Design
• Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data
• Federated Edge Learning with Misaligned Over-The-Air Computation
• Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
• Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
• Separation of Powers in Federated Learning
• Federated Extra-Trees with Privacy Preserving
• Distributed Learning on Heterogeneous Resource-Constrained Devices
• Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
• Practical Federated Learning without a Server
• TiFL: A Tier-based Federated Learning System
• ABC-FL: Anomalous and Benign client Classification in Federated Learning
• Federated Learning With Highly Imbalanced Audio Data
• One-shot Distibuted Algorithm for PCA with RBF Kernels
• Algorithm Fairness in AI for Medicine and Healthcare
• Federated Survival Analysis with Discrete-Time Cox Models
• A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
• Distribution-Free Federated Learning with Conformal Predictions
• Real-Time Decentralized knowledge Transfer at the Edge
• Exact Support Recovery in Federated Regression with One-shot Communication
• An Operator Splitting View of Federated Learning
• Machine Intelligence at the Edge with Learning Centric Power Allocation
• Generating private data with user customization
• FlexPD: A Flexible Framework Of First-Order Primal-Dual Algorithms for Distributed Optimization
• CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability
• Minimax Bounds for Distributed Logistic Regression
• Towards Bidirectional Protection in Federated Learning
• QuPeL: Quantized Personalization with Applications to Federated Learning
• DPCOVID: Privacy-Preserving Federated Covid-19 Detection
• Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification
• Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
• Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
• Client Adaptation improves Federated Learning with Simulated Non-IID Clients
• Adapting to Function Difficulty and Growth Conditions in Private Optimization
• Efficiently Distributed Federated Learning
• Dynamic Attention-based Communication-Efficient Federated Learning
• A Hybrid-Order Distributed SGD Method for Non-Convex Optimization to Balance Communication Overhead, Computational Complexity, and Convergence Rate
• D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge
• Accelerating Federated Learning in Heterogeneous Data and Computational Environments
• Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud
• Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
• Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence
• Galaxy Learning -- A Position Paper
• Model Aggregation via Good-Enough Model Spaces
• Addressing Class Variable Imbalance in Federated Semi-supervised Learning
• Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems: A Tutorial Overview
• Asynchronous Federated Learning for Sensor Data with Concept Drift
• Bayesian Over-The-Air Computation
• Cooperative Learning via Federated Distillation over Fading Channels
• Optimal Complexity in Decentralized Training
• Making Asynchronous Stochastic Gradient Descent Work for Transformers
• Flimma: a federated and privacy-preserving tool for differential gene expression analysis
• Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future
• PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks
• Differentially Private Federated Learning via Inexact ADMM
• Reaching Data Confidentiality and Model Accountability on the CalTrain
• NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
• EasyFL: A Low-code Federated Learning Platform For Dummies
• Federated Learning of User Verification Models Without Sharing Embeddings
• Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
• FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting
• Federated Mimic Learning for Privacy Preserving Intrusion Detection
• Fed2: Feature-Aligned Federated Learning
• Optimization-Based GenQSGD for Federated Edge Learning
• Federated Dynamic Neural Network for Deep MIMO Detection
• Sample-based Federated Learning via Mini-batch SSCA
• An Expectation-Maximization Perspective on Federated Learning
• A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS
• FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
• Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks
• Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping
• Federated Learning via Plurality Vote
• Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization
• FedDQ: Communication-Efficient Federated Learning with Descending Quantization
• Neural Tangent Kernel Empowered Federated Learning
• Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
• FedRAD: Federated Robust Adaptive Distillation
• WAFFLE: Weighted Averaging for Personalized Federated Learning
• Local Learning at the Network Edge for Efficient & Secure Real-Time Predictive Analytics
• Federated Submodel Optimization for Hot and Cold Data Features
• Federated Few-Shot Learning with Adversarial Learning
• Statistical Estimation and Inference via Local SGD in Federated Learning
• On-device Federated Learning with Flower
• Communication Efficient Federated Learning with Adaptive Quantization
• Federated Myopic Community Detection with One-shot Communication
• A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks
• Federated Unbiased Learning to Rank
• Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
• The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting
• AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
• Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
• H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
• Spatial Privacy-aware VR streaming
• Robust Federated Learning by Mixture of Experts
• FedPower: Privacy-Preserving Distributed Eigenspace Estimation
• Decentralized Non-Convex Learning with Linearly Coupled Constraints
• A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices
• CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning
• CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
• Enabling Binary Neural Network Training on the Edge
• A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
• FedProf: Selective Federated Learning with Representation Profiling
• Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity
• Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
• Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacy
• Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks
• Probabilistic Federated Learning of Neural Networks Incorporated with Global Posterior Information
• Federated Learning as a Mean-Field Game
• Federated Learning with Dynamic Transformer for Text to Speech
• Optimising cost vs accuracy of decentralised analytics in fog computing environments
• DEED: A General Quantization Scheme for Communication Efficiency in Bits
• Interpretable collaborative data analysis on distributed data
• Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures
• Direct Federated Neural Architecture Search
• Towards Differentially Private Text Representations
• Security and Privacy Preserving Deep Learning
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
• Asynchronous Distributed Optimization with Stochastic Delays
• The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset
• Data-Free Evaluation of User Contributions in Federated Learning
• Domain Adaptation without Model Transferring
• Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
• Differential Privacy Meets Federated Learning under Communication Constraints
• Meta Clustering for Collaborative Learning
• Utility Fairness for the Differentially Private Federated Learning
• Prune2Edge: A Multi-Phase Pruning Pipelines to Deep Ensemble Learning in IIoT
• Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
• Machine Learning on Volatile Instances
• GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model
• Federating for Learning Group Fair Models
• Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
• Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
• Task-Adaptive Incremental Learning for Intelligent Edge Devices
• Big Data Intelligence Using Distributed Deep Neural Networks
• Bandwidth Slicing to Boost Federated Learning in Edge Computing
• ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
• Meta Matrix Factorization for Federated Rating Predictions
• Robust Federated Learning with Noisy Communication
• DVFL: A Vertical Federated Learning Method for Dynamic Data
• Scalable Federated Learning over Passive Optical Networks
• Differentially Private Federated Variational Inference
• A Federated Data-Driven Evolutionary Algorithm
• Matrix Sketching for Secure Collaborative Machine Learning
• FDNAS: Improving Data Privacy and Model Diversity in AutoML
• Distributed Sparse SGD with Majority Voting
• Information-Theoretic Perspective of Federated Learning
• Communication-Censored Distributed Stochastic Gradient Descent
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks
• Knowledge Transferring via Model Aggregation for Online Social Care
• Simeon -- Secure Federated Machine Learning Through Iterative Filtering
• SecEL: Privacy-Preserving, Verifiable and Fault-Tolerant Edge Learning for Autonomous Vehicles
• Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks
• CADA: Communication-Adaptive Distributed Adam
• Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data
• Introducing Noise in Decentralized Training of Neural Networks
• Adaptive Federated Learning With Gradient Compression in Uplink NOMA
• FedNS: Improving Federated Learning for collaborative image classification on mobile clients
• Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review
• Local Methods with Adaptivity via Scaling
• An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack
• Spatio-Temporal Federated Learning for Massive Wireless Edge Networks
• Multiple Classification with Split Learning
• Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?
• Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning
• Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning
• Federated Classification using Parsimonious Functions in Reproducing Kernel Hilbert Spaces
• Infinitely Divisible Noise in the Low Privacy Regime
• On Data Efficiency of Meta-learning
• Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
• Semi-Federated Learning
• On the Fairness of Swarm Learning in Skin Lesion Classification
• Data Aggregation Techniques for Internet of Things
• Cost-efficient and Skew-aware Data Scheduling for Incremental Learning in 5G Network
• Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
• Federated Contrastive Learning for Decentralized Unlabeled Medical Images
• Toward Smart Security Enhancement of Federated Learning Networks
• Model Linkage Selection for Cooperative Learning
• Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
• Toward Communication Efficient Adaptive Gradient Method
• Distantly Supervised Relation Extraction in Federated Settings
• On Second-order Optimization Methods for Federated Learning
• Energy-Efficient Multi-Orchestrator Mobile Edge Learning
• FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning
• Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning
• Canoe : A System for Collaborative Learning for Neural Nets
• Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
• Towards More Efficient Federated Learning with Better Optimization Objects
• Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT
• Together or Alone: The Price of Privacy in Collaborative Learning
• Efficient Byzantine-Resilient Stochastic Gradient Desce
• Sensing and Mapping for Better Roads: Initial Plan for Using Federated Learning and Implementing a Digital Twin to Identify the Road Conditions in a Developing Country -- Sri Lanka
• Design and Analysis of Uplink and Downlink Communications for Federated Learning
• MyMigrationBot: A Cloud-based Facebook Social Chatbot for Migrant Populations
• Federated Learning from Small Datasets
• Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization and Statistical Learning
• Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
• Can Federated Learning Save The Planet?
• Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications
• Trends in Blockchain and Federated Learning for Data Sharing in Distributed Platforms
• AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
• Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
• ASCII: ASsisted Classification with Ignorance Interchange
• Dynamic Clustering in Federated Learning
• Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
• Gradient-Leakage Resilient Federated Learning
• Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data
• Collaborative Edge Learning in MIMO-NOMA Uplink Transmission Environment
• Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
• Test-time Collective Prediction
• Over-the-Air Decentralized Federated Learning
• A Privacy-Preserving and Trustable Multi-agent Learning Framework
• Linear Speedup in Personalized Collaborative Learning
• Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems
• Accumulative Poisoning Attacks on Real-time Data
• Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
• Pronto: Federated Task Scheduling
• Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection
• Network-Density-Controlled Decentralized Parallel Stochastic Gradient Descent in Wireless Systems
• Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
• Latency Analysis of Consortium Blockchained Federated Learning
• FedOptima: Optimizing Resource Utilization in Federated Learning
• Multi-resource allocation for federated settings: A non-homogeneous Markov chain model
• DEAL: Decremental Energy-Aware Learning in a Federated System
• Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation
• Random gradient extrapolation for distributed and stochastic optimization
• Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation
• Secure Distributed Training at Scale
• MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
• Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
• Compositional federated learning: Applications in distributionally robust averaging and meta learning
• Architecture Matters: Investigating the Influence of Differential Privacy on Neural Network Design
• Escaping Saddle Points with Compressed SGD
• Prior-Independent Auctions for the Demand Side of Federated Learning
• Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks
• Probabilistic Inference for Learning from Untrusted Sources
• A method of supervised learning from conflicting data with hidden contexts
• Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks
• Taming Distrust in the Decentralized Internet with PIXIU
• HADFL: Heterogeneity-aware Decentralized Federated Learning Framework
• Iterated Vector Fields and Conservatism, with Applications to Federated Learning
• Decentralized Wireless Federated Learning with Differential Privacy
• A Personalized Federated Learning Algorithm: an Application in Anomaly Detection
• Distributed Networked Learning with Correlated Data
• Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
• Distributed Sparse Feature Selection in Communication-Restricted Networks
• To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
• Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
• Loosely Coupled Federated Learning Over Generative Models
• Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
• Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
• Oscars: Adaptive Semi-Synchronous Parallel Model for Distributed Deep Learning with Global View
• Synthetic Data Generation for Economists
• Distributed Parameter Estimation in Randomized One-hidden-layer Neural Networks
• Network Consensus with Privacy: A Secret Sharing Method
• TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
• Trade-offs of Local SGD at Scale: An Empirical Study
• Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
• CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning
• Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
• Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning