Federated Learning: Strategies for Improving Communication Efficiency

Abstract

Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model. The typical clients in this setting are mobile phones, and communication efficiency is of the utmost importance. In this paper, we propose two ways to reduce the uplink communication costs: structured updates, where we directly learn an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, where we learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling before sending it to the server. Experiments on both convolutional and recurrent networks show that the proposed methods can reduce the communication cost by two orders of magnitude.

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• Real-time Cyberattack Detection with Collaborative Learning for Blockchain Networks
• pFedGame -- Decentralized Federated Learning using Game Theory in Dynamic Topology
• Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid Federated Approach
• MC${}^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
• Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
• ESMFL: Efficient and Secure Models for Federated Learning
• Complex-valued Federated Learning with Differential Privacy and MRI Applications
• Voting-based Approaches For Differentially Private Federated Learning
• Distributed Fixed Point Methods with Compressed Iterates
• Application-driven Privacy-preserving Data Publishing with Correlated Attributes
• Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
• GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
• Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
• Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning
• Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning
• Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning
• Is Network the Bottleneck of Distributed Training?
• Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent
• Pufferfish: Communication-efficient Models At No Extra Cost
• Vertical federated learning based on DFP and BFGS
• Federated Nonconvex Sparse Learning
• On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
• Resource-Constrained Federated Learning with Heterogeneous Labels and Models
• Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
• 2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection
• Centralized vs Decentralized Federated Learning: A trade-off performance analysis
• FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning
• Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets
• Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity
• Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications
• Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
• Federated Learning in Adversarial Settings
• Addressing Class Imbalance in Federated Learning
• Shredder: Learning Noise Distributions to Protect Inference Privacy
• 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
• SLSGD: Secure and Efficient Distributed On-device Machine Learning
• Estimation of Individual Device Contributions for Incentivizing Federated Learning
• Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
• Wireless MapReduce Distributed Computing
• Optimal Gradient Quantization Condition for Communication-Efficient Distributed Training
• A decentralized aggregation mechanism for training deep learning models using smart contract system for bank loan prediction
• Private Language Model Adaptation for Speech Recognition
• Distributed Machine Learning through Heterogeneous Edge Systems
• Training Neural Networks with Fixed Sparse Masks
• Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks
• Practical Privacy Preserving POI Recommendation
• Trends and Advancements in Deep Neural Network Communication
• Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging
• LocalNewton: Reducing Communication Bottleneck for Distributed Learning
• FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devices
• Policy-Based Federated Learning
• NNStreamer: Stream Processing Paradigm for Neural Networks, Toward Efficient Development and Execution of On-Device AI Applications
• PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
• R-GAP: Recursive Gradient Attack on Privacy
• MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks
• Proximal and Federated Random Reshuffling
• An Element-Wise Weights Aggregation Method for Federated Learning
• Aggregating Low Rank Adapters in Federated Fine-tuning
• An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning
• A Federated Random Forest Solution for Secure Distributed Machine Learning
• Federated Acoustic Modeling For Automatic Speech Recognition
• Cost-Effective Federated Learning Design
• FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
• Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
• Federated Crowdsensing: Framework and Challenges
• Over-the-Air Federated Learning with Retransmissions (Extended Version)
• Federated Natural Language Generation for Personalized Dialogue System
• Local Differential Privacy in Decentralized Optimization
• A Demonstration of Smart Doorbell Design Using Federated Deep Learning
• Exploiting Heterogeneity in Robust Federated Best-Arm Identification
• Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models
• Global Multiclass Classification and Dataset Construction via Heterogeneous Local Experts
• Critical Learning Periods in Federated Learning
• Game Theory and Machine Learning in UAVs-Assisted Wireless Communication Networks: A Survey
• Learning Structured Distributions From Untrusted Batches: Faster and Simpler
• Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
• Assisted Learning for Organizations with Limited Imbalanced Data
• AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning
• Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft
• DPD-InfoGAN: Differentially Private Distributed InfoGAN
• Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
• FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
• "Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT
• Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach
• Federated Unlearning
• Communication-Efficient Agnostic Federated Averaging
• High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
• Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
• Privacy-Preserving Federated Learning on Partitioned Attributes
• Coded Computing for Master-Aided Distributed Computing Systems
• MeDaS: An open-source platform as service to help break the walls between medicine and informatics
• Robust Coreset Construction for Distributed Machine Learning
• Federated Multi-task Hierarchical Attention Model for Sensor Analytics
• deepSELF: An Open Source Deep Self End-to-End Learning Framework
• Federated Generative Adversarial Learning
• Performance Analysis and Characterization of Training Deep Learning Models on Mobile Devices
• Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges
• 1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
• Linear Regression over Networks with Communication Guarantees
• Privacy-Aware Data Acquisition under Data Similarity in Regression Markets
• Real-Time Decentralized knowledge Transfer at the Edge
• Federated Edge Learning with Misaligned Over-The-Air Computation
• Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
• Device-Cloud Collaborative Learning for Recommendation
• PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks
• Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
• Optimal Complexity in Decentralized Training
• TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
• The Internet of Things as a Deep Neural Network
• Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data
• Federated Learning With Highly Imbalanced Audio Data
• FLRA: A Reference Architecture for Federated Learning Systems
• Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
• Reward-Based 1-bit Compressed Federated Distillation on Blockchain
• Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
• Dynamic Attention-based Communication-Efficient Federated Learning
• Asynchronous Federated Learning for Sensor Data with Concept Drift
• FlexPD: A Flexible Framework Of First-Order Primal-Dual Algorithms for Distributed Optimization
• Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
• More Industry-friendly: Federated Learning with High Efficient Design
• Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
• Galaxy Learning -- A Position Paper
• Exact Support Recovery in Federated Regression with One-shot Communication
• Client Adaptation improves Federated Learning with Simulated Non-IID Clients
• Learn distributed GAN with Temporary Discriminators
• Adaptive Distillation for Decentralized Learning from Heterogeneous Clients
• Accelerating Federated Learning in Heterogeneous Data and Computational Environments
• Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud
• Research Directions in Democratizing Innovation through Design Automation, One-Click Manufacturing Services and Intelligent Machines
• Minimax Bounds for Distributed Logistic Regression
• TiFL: A Tier-based Federated Learning System
• Federated Extra-Trees with Privacy Preserving
• Towards Tight Communication Lower Bounds for Distributed Optimisation
• Data-Free Evaluation of User Contributions in Federated Learning
• Robust Federated Learning with Noisy Communication
• FedProf: Selective Federated Learning with Representation Profiling
• Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
• Approximate Wireless Communication for Lossy Gradient Updates in IoT Federated Learning
• Secure Machine Learning over Relational Data
• Federating for Learning Group Fair Models
• Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
• FDNAS: Improving Data Privacy and Model Diversity in AutoML
• FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
• Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
• Neural Parameter Allocation Search
• Meta Clustering for Collaborative Learning
• Sparse sketches with small inversion bias
• FedNS: Improving Federated Learning for collaborative image classification on mobile clients
• Differential Privacy Meets Federated Learning under Communication Constraints
• Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
• Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data
• Communication-Censored Distributed Stochastic Gradient Descent
• A Quantitative Metric for Privacy Leakage in Federated Learning
• Efficient Client Contribution Evaluation for Horizontal Federated Learning
• Communication-efficient Byzantine-robust distributed learning with statistical guarantee
• Deep Learning Framework for Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems
• Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach
• Federated Myopic Community Detection with One-shot Communication
• Private Dataset Generation Using Privacy Preserving Collaborative Learning
• Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
• Faster Secure Data Mining via Distributed Homomorphic Encryption
• Knowledge Transferring via Model Aggregation for Online Social Care
• Fair and autonomous sharing of federate learning models in mobile Internet of Things
• FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors
• Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
• Shuffle-Exchange Brings Faster: Reduce the Idle Time During Communication for Decentralized Neural Network Training
• Marketplace for AI Models
• Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guarantees
• New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
• SEEC: Semantic Vector Federation across Edge Computing Environments
• Decentralized Non-Convex Learning with Linearly Coupled Constraints
• Privacy-Preserving Machine Learning Training in Aggregation Scenarios
• When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
• Distributed Optimization on Riemannian Manifolds for multi-agent networks
• H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning
• Machine Learning on Volatile Instances
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing
• Prune2Edge: A Multi-Phase Pruning Pipelines to Deep Ensemble Learning in IIoT
• Local Methods with Adaptivity via Scaling
• Federated Learning without Revealing the Decision Boundaries
• Mobility-Aware Routing and Caching: A Federated Learning Assisted Approach
• Kalman Filter Aided Federated Koopman Learning
• Integrated 3C in NOMA-enabled Remote-E-Health Systems
• An Experiment Study on Federated LearningTestbed
• Management of Resource at the Network Edge for Federated Learning
• Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
• Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
• Gradient Masked Federated Optimization
• OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints
• On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator
• Associative Convolutional Layers
• MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
• Mixing Deep Learning and Multiple Criteria Optimization: An Application to Distributed Learning with Multiple Datasets
• FLBench: A Benchmark Suite for Federated Learning
• Joint Coreset Construction and Quantization for Distributed Machine Learning
• Decentralize the feedback infrastructure!
• MyMigrationBot: A Cloud-based Facebook Social Chatbot for Migrant Populations
• ASCII: ASsisted Classification with Ignorance Interchange
• Towards More Efficient Federated Learning with Better Optimization Objects
• Randomized Controlled Trials without Data Retention
• Canoe : A System for Collaborative Learning for Neural Nets
• Gradient-Leakage Resilient Federated Learning
• Multimedia Edge Computing
• Multi-resource allocation for federated settings: A non-homogeneous Markov chain model
• Toward Communication Efficient Adaptive Gradient Method
• Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection
• Model Linkage Selection for Cooperative Learning
• Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
• Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients
• Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning
• Accumulative Poisoning Attacks on Real-time Data
• AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
• TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data
• MANELA: A Multi-Agent Algorithm for Learning Network Embeddings
• Distributed Networked Learning with Correlated Data
• A Personalized Federated Learning Algorithm: an Application in Anomaly Detection
• Semi-Federated Learning
• Towards Heterogeneous Clients with Elastic Federated Learning
• CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning
• Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
• Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?
• FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning
• Federated Marginal Personalization for ASR Rescoring
• Topology-aware Differential Privacy for Decentralized Image Classification
• Design and Analysis of Uplink and Downlink Communications for Federated Learning
• Distantly Supervised Relation Extraction in Federated Settings
• Pronto: Federated Task Scheduling
• Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
• $C^{3}DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
• The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication
• Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
• Loosely Coupled Federated Learning Over Generative Models
• Privacy-Preserving Bandits
• DEAL: Decremental Energy-Aware Learning in a Federated System
• Spatio-Temporal Federated Learning for Massive Wireless Edge Networks
• Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
• GraphFederator: Federated Visual Analysis for Multi-party Graphs
• AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI
• Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications
• Multiple Kernel-Based Online Federated Learning
• Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks
• 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
• Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
• Federated Classification using Parsimonious Functions in Reproducing Kernel Hilbert Spaces