Federated Machine Learning: Concept and Applications

Abstract

Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allow knowledge to be shared without compromising user privacy.

References in corpus (14)

• Deep Learning with Differential Privacy
• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Learning with Non-IID Data
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
• How To Backdoor Federated Learning
• Differentially Private Federated Learning: A Client Level Perspective
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• CryptoDL: Deep Neural Networks over Encrypted Data
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
• Adaptive Federated Learning in Resource Constrained Edge Computing Systems
• Entity Resolution and Federated Learning get a Federated Resolution
• Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

Cited by in corpus (91)

• FedKD: Communication Efficient Federated Learning via Knowledge Distillation
• Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
• Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms
• FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
• Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
• AI based Service Management for 6G Green Communications
• Federated Reinforcement Learning: Techniques, Applications, and Open Challenges
• FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC
• Inverting Gradients -- How easy is it to break privacy in federated learning?
• Privacy Preserving Distributed Machine Learning with Federated Learning
• A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets
• Federated Forest
• Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
• OpenFL: An open-source framework for Federated Learning
• Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks
• Federated Continual Learning via Knowledge Fusion: A Survey
• FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
• Abnormal Client Behavior Detection in Federated Learning
• FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
• A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
• Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
• An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
• Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
• Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
• An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems
• Active Learning for Network Traffic Classification: A Technical Study
• DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering
• Improving Fairness for Data Valuation in Horizontal Federated Learning
• Federated Multi-view Matrix Factorization for Personalized Recommendations
• COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis
• A Communication Efficient Collaborative Learning Framework for Distributed Features
• SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
• Federated Contrastive Learning for Volumetric Medical Image Segmentation
• BlindFL: Vertical Federated Machine Learning without Peeking into Your Data
• ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning
• Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach
• ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users
• Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
• Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
• Time-sensitive Learning for Heterogeneous Federated Edge Intelligence
• Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
• BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction
• Research and Education Towards Smart and Sustainable World
• A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency
• Towards Ubiquitous AI in 6G with Federated Learning
• Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
• CADeSH: Collaborative Anomaly Detection for Smart Homes
• PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning
• Practical Vertical Federated Learning with Unsupervised Representation Learning
• Federated Fuzzy Neural Network with Evolutionary Rule Learning
• Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices
• The Next Decade of Telecommunications Artificial Intelligence
• Federated Bandit: A Gossiping Approach
• Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles
• Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
• Training Fair Models in Federated Learning without Data Privacy Infringement
• Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble
• Decentralised and collaborative machine learning framework for IoT
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack
• Self-supervised Cross-silo Federated Neural Architecture Search
• Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data
• Turning Privacy-preserving Mechanisms against Federated Learning
• Accelerating Vertical Federated Learning
• Dynamic backdoor attacks against federated learning
• Gradient Descent with Compressed Iterates
• Network Support for High-performance Distributed Machine Learning
• Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach
• FedSiam: Towards Adaptive Federated Semi-Supervised Learning
• Federated Learning -- Methods, Applications and beyond
• Hijack Vertical Federated Learning Models As One Party
• Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA
• Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
• Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud
• Benchmarking Differential Privacy and Federated Learning for BERT Models
• Personalised Federated Learning On Heterogeneous Feature Spaces
• OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
• A Fusion-Denoising Attack on InstaHide with Data Augmentation
• AVDDPG: Federated reinforcement learning applied to autonomous platoon control
• Vertical federated learning based on DFP and BFGS
• Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor
• Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation
• Data-aided Sensing where Communication and Sensing Meet: An Introduction
• FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
• A Demonstration of Smart Doorbell Design Using Federated Deep Learning
• A Framework for Verifiable and Auditable Federated Anomaly Detection
• FedSmart: An Auto Updating Federated Learning Optimization Mechanism
• One-shot Distibuted Algorithm for PCA with RBF Kernels
• Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
• BadVFL: Backdoor Attacks in Vertical Federated Learning
• Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning
• Optimal Transport Aggregation for Distributed Mixture-of-Experts