Federated Learning

Part 1: Introduction

Federated Learning Comic
Federated Learning: Collaborative Machine Learning without Centralized Training Data
GDPR, Data Shotrage and AI (AAAI-19)
Federated Learning: Machine Learning on Decentralized Data (Google I/O'19)
Federated Learning White Paper V1.0
Federated learning: distributed machine learning with data locality and privacy

Part 2: Survey

Federated Learning: Challenges, Methods, and Future Directions
Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Advances and Open Problems in Federated Learning
Federated Machine Learning: Concept and Applications
Threats to Federated Learning: A Survey
Survey of Personalization Techniques for Federated Learning
SECure: A Social and Environmental Certificate for AI Systems
From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
A Review of Privacy Preserving Federated Learning for Private IoT Analytics
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing
An Introduction to Communication Efficient Edge Machine Learning
Federated Learning for Healthcare Informatics
Federated Learning for Coalition Operations
No Peek: A Survey of private distributed deep learning
Communication-Efficient Edge AI: Algorithms and Systems

Part 3: Benchmarks

LEAF: A Benchmark for Federated Settings(https://github.com/TalwalkarLab/leaf) [Recommend]
A Performance Evaluation of Federated Learning Algorithms
Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking

Part 4: Converge

4.1 Model Aggregation

One-Shot Federated Learning
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating (NIPS 2019 Workshop)
Bayesian Nonparametric Federated Learning of Neural Networks (ICML 2019)
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning (ICLR 2021)
Agnostic Federated Learning (ICML 2019)
Federated Learning with Matched Averaging (ICLR 2020)
Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications

4.2 Convergence Research

A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication （NIPS 2018）
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning (ICLR 2021)
FetchSGD: Communication-Efficient Federated Learning with Sketching
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis (ICML 2021)
Federated Multi-armed Bandits with Personalization (AISTATS 2021)
Federated Learning with Compression: Unified Analysis and Sharp Guarantees (AISTATS 2021)
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning (AISTATS 2021)
Towards Flexible Device Participation in Federated Learning (AISTATS 2021)
Fed2: Feature-Aligned Federated Learning (KDD 2021)
Federated Optimization for Heterogeneous Networks
On the Convergence of FedAvg on Non-IID Data [OpenReview]
Communication Efficient Decentralized Training with Multiple Local Updates
Local SGD Converges Fast and Communicates Little
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning (AAAI 2018）
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization (ICML 2019）
Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data （NIPS 2019 Workshop)

4.3 Statistical Heterogeneity

FedPD: A Federated Learning Framework with Optimal Rates andAdaptivity to Non-IID Data
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization (ICLR 2021)
FedMix: Approximation of Mixup under Mean Augmented Federated Learning (ICLR 2021)
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients (ICLR 2021)
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data (KDD 2021)
FedMatch: Federated Learning Over Heterogeneous Question Answering Data (CIKM 2021)
Decentralized Learning of Generative Adversarial Networks from Non-iid Data
Towards Class Imbalance in Federated Learning
Communication-Efficient On-Device Machine Learning:Federated Distillation and Augmentationunder Non-IID Private Data
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Federated Adversarial Domain Adaptation
Federated Learning with Only Positive Labels
Federated Learning with Non-IID Data
The Non-IID Data Quagmire of Decentralized Machine Learning
Robust and Communication-Efficient Federated Learning from Non-IID Data (IEEE transactions on neural networks and learning systems)
FedMD: Heterogenous Federated Learning via Model Distillation (NIPS 2019 Workshop)
First Analysis of Local GD on Heterogeneous Data
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning
Improving Federated Learning Personalization via Model Agnostic Meta Learning (NIPS 2019 Workshop)
Personalized Federated Learning with First Order Model Optimization (ICLR 2021)
LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on Medical Data
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods
Overcoming Forgetting in Federated Learning on Non-IID Data （NIPS 2019 Workshop)
FedMAX: Activation Entropy Maximization Targeting Effective Non-IID Federated Learning （NIPS 2019 Workshop)
Adaptive Federated Optimization.(ICLR 2021 (Under Review))
Stochastic, Distributed and Federated Optimization for Machine Learning. FL PhD Thesis. By Jakub
Collaborative Deep Learning in Fixed Topology Networks
FedCD: Improving Performance in non-IID Federated Learning.
Life Long Learning: FedFMC: Sequential Efficient Federated Learning on Non-iid Data.
Robust Federated Learning: The Case of Affine Distribution Shifts.
Exploiting Shared Representations for Personalized Federated Learning (ICML 2021)
Personalized Federated Learning using Hypernetworks (ICML 2021)
Ditto: Fair and Robust Federated Learning Through Personalization (ICML 2021)
Data-Free Knowledge Distillation for Heterogeneous Federated Learning (ICML 2021)
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning (ICML 2021)
Heterogeneity for the Win: One-Shot Federated Clustering (ICML 2021)
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning (ICML 2021)
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity (ICML 2021)
Federated Learning of User Verification Models Without Sharing Embeddings (ICML 2021)
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning (ICML 2021)
Ensemble Distillation for Robust Model Fusion in Federated Learning.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning.
An Efficient Framework for Clustered Federated Learning.
Continual Local Training for Better Initialization of Federated Models.
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data.
Global Multiclass Classification from Heterogeneous Local Models.
Multi-Center Federated Learning.
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning (ICLR 2021)
(*) FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning. CMU ECE.
(*) Adaptive Personalized Federated Learning
Semi-Federated Learning
Device Heterogeneity in Federated Learning: A Superquantile Approach.
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
Three Approaches for Personalization with Applications to Federated Learning
Personalized Federated Learning: A Meta-Learning Approach
Towards Federated Learning: Robustness Analytics to Data Heterogeneity
Salvaging Federated Learning by Local Adaptation
FOCUS: Dealing with Label Quality Disparity in Federated Learning.
Overcoming Noisy and Irrelevant Data in Federated Learning.(ICPR 2020)
Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning.
(*) Think Locally, Act Globally: Federated Learning with Local and Global Representations. NeurIPS 2019 Workshop on Federated Learning distinguished student paper award
Federated Learning with Personalization Layers
Federated Evaluation of On-device Personalization
Measure Contribution of Participants in Federated Learning
(*) Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Multi-hop Federated Private Data Augmentation with Sample Compression
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters
Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity
Client Adaptation improves Federated Learning with Simulated Non-IID Clients

4.4 Adaptive Aggregation

Asynchronous Federated Learning for Geospatial Applications (ECML PKDD Workshop 2018）
Asynchronous Federated Optimization
Adaptive Federated Learning in Resource Constrained Edge Computing Systems (IEEE Journal on Selected Areas in Communications, 2019）
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation (ICML 2021)

Part 5: Security

5.1 Adversarial Attacks

Can You Really Backdoor Federated Learning? (NeruIPS 2019)
Model Poisoning Attacks in Federated Learning (NIPS workshop 2018）
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies.
How To Backdoor Federated Learning.(AISTATS 2020)
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning.(ACM CCS 2017)
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Deep Leakage from Gradients.(NIPS 2019)
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning.
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning.(INFOCOM 2019)
Analyzing Federated Learning through an Adversarial Lens.(ICML 2019）
Mitigating Sybils in Federated Learning Poisoning.(RAID 2020)
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets.(AAAI 2019)
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning.
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning.
Backdoor Attacks on Federated Meta-Learning
Towards Realistic Byzantine-Robust Federated Learning.
Data Poisoning Attacks on Federated Machine Learning.
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning.
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data.
FedMGDA+: Federated Learning meets Multi-objective Optimization.
Free-rider Attacks on Model Aggregation in Federated Learning (AISTATS 2021)
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications.
Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework.
BASGD: Buffered Asynchronous SGD for Byzantine Learning.
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees.
Learning to Detect Malicious Clients for Robust Federated Learning.
Robust Aggregation for Federated Learning.
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems.
Attack-Resistant Federated Learning with Residual-based Reweighting.
Free-riders in Federated Learning: Attacks and Defenses.
Robust Federated Learning with Noisy Communication.
Abnormal Client Behavior Detection in Federated Learning.
Eavesdrop the Composition Proportion of Training Labels in Federated Learning.
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging.
An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning.
Secure Distributed On-Device Learning Networks With Byzantine Adversaries.
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances.
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting.
Inverting Gradients - How easy is it to break privacy in federated learning?

5.2 Data Privacy and Confidentiality

Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning （NIPS 2019 Workshop)
Quantification of the Leakage in Federated Learning

Part 6: Communication Efficiency

Communication-Efficient Learning of Deep Networks from Decentralized Data](https://github.com/roxanneluo/Federated-Learning) [Google] [Must Read]
Two-Stream Federated Learning: Reduce the Communication Costs (2018 IEEE VCIP)
Federated Learning Based on Dynamic Regularization (ICLR 2021)
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms (ICLR 2021)
Adaptive Federated Optimization (ICLR 2021)
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization （NIPS 2019）
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training (ICLR 2018)
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication
A Communication Efficient Collaborative Learning Framework for Distributed Features （NIPS 2019 Workshop)
Active Federated Learning （NIPS 2019 Workshop)
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction （NIPS 2019 Workshop)
Gradient Descent with Compressed Iterates （NIPS 2019 Workshop)
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Exact Support Recovery in Federated Regression with One-shot Communication
DEED: A General Quantization Scheme for Communication Efficiency in Bits
Personalized Federated Learning with Moreau Envelopes
Towards Flexible Device Participation in Federated Learning for Non-IID Data.
A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization
FedSplit: An algorithmic framework for fast federated optimization
Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction
On the Outsized Importance of Learning Rates in Local Update Methods.
Federated Learning with Compression: Unified Analysis and Sharp Guarantees.
From Local SGD to Local Fixed-Point Methods for Federated Learning
Federated Residual Learning.
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization.[ICML 2020]
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge (FedCS)
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor
Dynamic Federated Learning
Distributed Optimization over Block-Cyclic Data
Federated Composite Optimization (ICML 2021)
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability
Federated Learning of a Mixture of Global and Local Models
Faster On-Device Training Using New Federated Momentum Algorithm
FedDANE: A Federated Newton-Type Method
Distributed Fixed Point Methods with Compressed Iterates
Primal-dual methods for large-scale and distributed convex optimization and data analytics
Parallel Restarted SPIDER - Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
Representation of Federated Learning via Worst-Case Robust Optimization Theory
On the Convergence of Local Descent Methods in Federated Learning
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Accelerating Federated Learning via Momentum Gradient Descent
Robust Federated Learning in a Heterogeneous Environment
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Differentially Private Learning with Adaptive Clipping
Semi-Cyclic Stochastic Gradient Descent
Federated Optimization in Heterogeneous Networks
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Learning Rate Adaptation for Federated and Differentially Private Learning
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
Don’t Use Large Mini-Batches, Use Local SGD
Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD
Local SGD With a Communication Overhead Depending Only on the Number of Workers
Federated Accelerated Stochastic Gradient Descent
Tighter Theory for Local SGD on Identical and Heterogeneous Data
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Understanding Unintended Memorization in Federated Learning
eSGD: Communication Efficient Distributed Deep Learning on the Edge (USENIX 2018 Workshop)
CMFL: Mitigating Communication Overhead for Federated Learning

6.1 Compression

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Federated Learning: Strategies for Improving Communication Efficiency （NIPS2016 Workshop) [Google]
Natural Compression for Distributed Deep Learning
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
ATOMO: Communication-efficient Learning via Atomic Sparsification(NIPS 2018）
vqSGD: Vector Quantized Stochastic Gradient Descent
QSGD: Communication-efficient SGD via gradient quantization and encoding （NIPS 2017)
Federated Optimization: Distributed Machine Learning for On-Device Intelligence [Google]
Distributed Mean Estimation with Limited Communication (ICML 2017)
Randomized Distributed Mean Estimation: Accuracy vs Communication
Error Feedback Fixes SignSGD and other Gradient Compression Schemes (ICML 2019）
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning (ICML 2017)

Part 7: Personalized Federated Learning

7.1 Meta Learning

Federated Meta-Learning with Fast Convergence and Efficient Communication
Federated Meta-Learning for Recommendation
Adaptive Gradient-Based Meta-Learning Methods

7.2 Multi-task Learning

MOCHA: Federated Multi-Task Learning (NIPS 2017)
Variational Federated Multi-Task Learning
Federated Kernelized Multi-Task Learning
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints （NIPS 2019 Workshop)
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms

7.3 Hierarchical FL

Client-Edge-Cloud Hierarchical Federated Learning
(FL startup: Tongdun, HangZhou, China) Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework.
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
Hierarchical Federated Learning Across Heterogeneous Cellular Networks
Enhancing Privacy via Hierarchical Federated Learning
Federated learning with hierarchical clustering of local updates to improve training on non-IID data.
Federated Hierarchical Hybrid Networks for Clickbait Detection

7.4 Transfer Learning

Secure Federated Transfer Learning. IEEE Intelligent Systems 2018.
Secure and Efficient Federated Transfer Learning
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning.
Cooperative Learning via Federated Distillation over Fading Channels
(*) Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Federated Reinforcement Distillation with Proxy Experience Memory
Federated Continual Learning with Weighted Inter-client Transfer (ICML 2021)

Part 8 Decentralization & Incentive Mechanism

8.1 Decentralized

Communication Compression for Decentralized Training （NIPS 2018）
𝙳𝚎𝚎𝚙𝚂𝚚𝚞𝚎𝚎𝚣𝚎: Decentralization Meets Error-Compensated Compression
Central Server Free Federated Learning over Single-sided Trust Social Networks
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Multi-consensus Decentralized Accelerated Gradient Descent
Decentralized Bayesian Learning over Graphs.
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Matcha: Speeding Up Decentralized SGD via Matching Decomposition Sampling

8.2 Incentive Mechanism

Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory
Towards Fair Federated Learning (KDD 2021)
Federated Adversarial Debiasing for Fair and Transferable Representations (KDD 2021)
Motivating Workers in Federated Learning: A Stackelberg Game Perspective
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
Fair Resource Allocation in Federated Learning
FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC.(ICDCS 2020)
Toward an Automated Auction Framework for Wireless Federated Learning Services Market
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism
A Learning-based Incentive Mechanism forFederated Learning
A Crowdsourcing Framework for On-Device Federated Learning
Rewarding High-Quality Data via Influence Functions
Joint Service Pricing and Cooperative Relay Communication for Federated Learning
Measure Contribution of Participants in Federated Learning
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

Part 9: Vertical Federated Learning

A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression （NIPS 2019 Workshop)
SecureBoost: A Lossless Federated Learning Framework
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization (KDD 2021)
Large-scale Secure XGB for Vertical Federated Learning (CIKM 2021)
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Entity Resolution and Federated Learning get a Federated Resolution.
Multi-Participant Multi-Class Vertical Federated Learning
A Communication-Efficient Collaborative Learning Framework for Distributed Features
Asymmetrical Vertical Federated Learning
VAFL: a Method of Vertical Asynchronous Federated Learning (ICML workshop on FL, 2020)
SplitFed: When Federated Learning Meets Split Learning
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction
Stochastic Distributed Optimization for Machine Learning from Decentralized Features

Part 10: Wireless Communication and Cloud Computing

Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
Wireless Communications for Collaborative Federated Learning in the Internet of Things
Democratizing the Edge: A Pervasive Edge Computing Framework
UVeQFed: Universal Vector Quantization for Federated Learning
Federated Deep Learning Framework For Hybrid Beamforming in mm-Wave Massive MIMO
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints
A Secure Federated Learning Framework for 5G Networks
Federated Learning and Wireless Communications
Lightwave Power Transfer for Federated Learning-based Wireless Networks
Towards Ubiquitous AI in 6G with Federated Learning
Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
Network-Aware Optimization of Distributed Learning for Fog Computing
On the Design of Communication Efficient Federated Learning over Wireless Networks
Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
Scheduling for Cellular Federated Edge Learning with Importance and Channel.
Differentially Private Federated Learning for Resource-Constrained Internet of Things.
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks.
Gradient Estimation for Federated Learning over Massive MIMO Communication Systems
Adaptive Federated Learning With Gradient Compression in Uplink NOMA
Performance Analysis and Optimization in Privacy-Preserving Federated Learning
Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design
Federated Over-the-Air Subspace Learning and Tracking from Incomplete Data
Decentralized Federated Learning via SGD over Wireless D2D Networks
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Wireless Federated Learning with Local Differential Privacy
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation.
Learning from Peers at the Wireless Edge
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge
Communication Efficient Federated Learning over Multiple Access Channels
Convergence Time Optimization for Federated Learning over Wireless Networks
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks.(IEEE Internet of Things Journal. 2020)
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Federated learning with multichannel ALOHA
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Bandwidth Slicing to Boost Federated Learning in Edge Computing
Energy Efficient Federated Learning Over Wireless Communication Networks
Device Scheduling with Fast Convergence for Wireless Federated Learning
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
Reliable Federated Learning for Mobile Networks
Cell-Free Massive MIMO for Wireless Federated Learning
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
On Safeguarding Privacy and Security in the Framework of Federated Learning
Scheduling Policies for Federated Learning in Wireless Networks
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs
Energy-Efficient Radio Resource Allocation for Federated Edge Learning
Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System
Active Learning Solution on Distributed Edge Computing
Fast Uplink Grant for NOMA: a Federated Learning based Approach
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
Broadband Analog Aggregation for Low-Latency Federated Edge Learning
Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
Joint Service Pricing and Cooperative Relay Communication for Federated Learning
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Asynchronous Task Allocation for Federated and Parallelized Mobile Edge Learning
Ask to upload some data from client to server Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
Low-latency Broadband Analog Aggregation For Federated Edge Learning
Federated Learning over Wireless Fading Channels
Federated Learning via Over-the-Air Computation

Part 11: Federated with Deep learning

11.1 Neural Architecture Search(NAS)

FedNAS: Federated Deep Learning via Neural Architecture Search.(CVPR 2020)
Real-time Federated Evolutionary Neural Architecture Search.
Federated Neural Architecture Search.
Differentially-private Federated Neural Architecture Search.

11.2 Graph Neural Network(GNN)

SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure (Big Data)
GraphFederator: Federated Visual Analysis for Multi-party Graphs.
FedE: Embedding Knowledge Graphs in Federated Setting
ASFGNN: Automated Separated-Federated Graph Neural Network
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Peer-to-peer Federated Learning on Graphs
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
Secure Deep Graph Generation with Link Differential Privacy (IJCAI 2021)
Locally Private Graph Neural Networks (CCS 2021)
When Differential Privacy Meets Graph Neural Networks
Releasing Graph Neural Networks with Differential Privacy
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation (ICML 2021)
Decentralized Federated Graph Neural Networks (IJCAI 2021)
Federated Graph Classification over Non-IID Graphs (NeurIPS 2021)
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks (ICML 2021)
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks (ICLR 2021)
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling (KDD 2021)

Part 12: FL system & Library & Courses

12.1 System

Towards Federated Learning at Scale: System Design [Must Read]
Scaling Distributed Machine Learning with System and Algorithm Co-design
Demonstration of Federated Learning in a Resource-Constrained Networked Environment
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
FedML: A Research Library and Benchmark for Federated Machine Learning
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction.
Heterogeneity-Aware Federated Learning
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
[startup] Industrial Federated Learning -- Requirements and System Design
(*) TiFL: A Tier-based Federated Learning System.(HPDC 2020)
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach(ICDCS 2020)
Quantifying the Performance of Federated Transfer Learning
ELFISH: Resource-Aware Federated Learning on Heterogeneous Edge Devices
Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
BAFFLE : Blockchain Based Aggregator Free Federated Learning
Functional Federated Learning in Erlang (ffl-erl)
HierTrain: Fast Hierarchical Edge AI Learning With Hybrid Parallelism in Mobile-Edge-Cloud Computing
Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design
Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation
Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?
Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes

12.2 Courses

Applied Cryptography
A Brief Introduction to Differential Privacy
Deep Learning with Differential Privacy.
Building Safe A.I.
- A Tutorial for Encrypted Deep Learning
- Use Homomorphic Encryption (HE)
Private Deep Learning with MPC
- A Simple Tutorial from Scratch
- Use Multiparty Compuation (MPC)
Private Image Analysis with MPC
- Training CNNs on Sensitive Data
- Use SPDZ as MPC protocol

13.2 Secret Sharing

Simple Introduction to Sharmir's Secret Sharing and Lagrange Interpolation
Secret Sharing, Part 1: Shamir's Secret Sharing & Packed Variant
Secret Sharing, Part 2: Improve efficiency
Secret Sharing, Part 3

Part 13: Secure Multi-party Computation(MPC)

13.1 Differential Privacy

Learning Differentially Private Recurrent Language Models
Federated Learning with Bayesian Differential Privacy （NIPS 2019 Workshop)
Private Federated Learning with Domain Adaptation （NIPS 2019 Workshop)
cpSGD: Communication-efficient and differentially-private distributed SGD
Practical Secure Aggregation for Federated Learning on User-Held Data.（NIPS 2016 Workshop)
Differentially Private Federated Learning: A Client Level Perspective.（NIPS 2017 Workshop)
Exploiting Unintended Feature Leakage in Collaborative Learning.(S&P 2019)
A Hybrid Approach to Privacy-Preserving Federated Learning. (AISec 2019)
A generic framework for privacy preserving deep learning. (PPML 2018)
Federated Generative Privacy.（IJCAI 2019 FL Workshop)
Enhancing the Privacy of Federated Learning with Sketching.
https://aisec.cc/
Federated f-Differential Privacy (AISTATS 2021)
Shuffled Model of Differential Privacy in Federated Learning (AISTATS 2021)
Differentially Private Federated Knowledge Graphs Embedding (CIKM 2021)
Anonymizing Data for Privacy-Preserving Federated Learning.
Practical and Bilateral Privacy-preserving Federated Learning.
Decentralized Policy-Based Private Analytics.
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection. (DASFAA 2020)
Learn to Forget: User-Level Memorization Elimination in Federated Learning.
LDP-Fed: Federated Learning with Local Differential Privacy.（EdgeSys 2020)
PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks.
Local Differential Privacy based Federated Learning for Internet of Things.
Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise.
Decentralized Differentially Private Segmentation with PATE.（MICCAI 2020 Under Review)
Privacy Preserving Distributed Machine Learning with Federated Learning.
Exploring Private Federated Learning with Laplacian Smoothing.
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning.
Efficient Privacy Preserving Edge Computing Framework for Image Classification.
A Distributed Trust Framework for Privacy-Preserving Machine Learning.
Secure Byzantine-Robust Machine Learning.
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing.
Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control.
(*) Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties.
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators.
Federated Learning with Differential Privacy:Algorithms and Performance Analysis

13.2 Secret Sharing

Simple Introduction to Sharmir's Secret Sharing and Lagrange Interpolation
Secret Sharing, Part 1: Shamir's Secret Sharing & Packed Variant
Secret Sharing, Part 2: Improve efficiency
Secret Sharing, Part 3

Part 14: Applications

Federated Learning Approach for Mobile Packet Classification
Federated Learning for Ranking Browser History Suggestions (NIPS 2019 Workshop)

14.1 Healthcare

HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography (NIPS 2019 Workshop)
Learn Electronic Health Records by Fully Decentralized Federated Learning (NIPS 2019 Workshop)
FLOP: Federated Learning on Medical Datasets using Partial Networks (KDD 2021)
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records [News]
Federated learning of predictive models from federated Electronic Health Records.
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation
NVIDIA Clara Federated Learning to Deliver AI to Hospitals While Protecting Patient Data
What is Federated Learning
Split learning for health: Distributed deep learning without sharing raw patient data
Two-stage Federated Phenotyping and Patient Representation Learning (ACL 2019)
Federated Tensor Factorization for Computational Phenotyping (SIGKDD 2017)
FedHealth- A Federated Transfer Learning Framework for Wearable Healthcare (ICJAI 2019 workshop)
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data- A Feasibility Study on Brain Tumor Segmentation (MICCAI'18 Workshop)
Federated Patient Hashing (AAAI 2020)

14.2 Natual Language Processing

Google

Federated Learning for Mobile Keyboard Prediction
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Federated Learning Of Out-Of-Vocabulary Words
Federated Learning for Emoji Prediction in a Mobile Keyboard

Snips

Federated Learning for Wake Keyword Spotting [Blog] [Github]

14.3 Computer Vision

Performance Optimization for Federated Person Re-identification via Benchmark Analysis (ACMMM 2020) [Github]
Real-World Image Datasets for Federated Learning
FedVision- An Online Visual Object Detection Platform Powered by Federated Learning (IAAI20)
Federated Learning for Vision-and-Language Grounding Problems (AAAI20)

14.4 Recommendation

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Federated Meta-Learning with Fast Convergence and Efficient Communication
Secure Federated Matrix Factorization
DiFacto: Distributed Factorization Machines

14.5 Industrial

Turbofan POC: Predictive Maintenance of Turbofan Engines using Federated Learning
Turbofan Tycoon Simulation by Cloudera/FastForwardLabs
Firefox Search Bar

Part 15: Organizations and Companies

15.1 国内篇

微众银行开源 FATE 框架.

Qiang Yang, Tianjian Chen, Yang Liu, Yongxin Tong.

《Federated machine learning: Concept and applications》
《Secureboost: A lossless federated learning framework》

字节跳动开源 FedLearner 框架.

Jiankai Sun, Weihao Gao, Hongyi Zhang, Junyuan Xie.《Label Leakage and Protection in Two-party Split learning》

华控清交 PrivPy 多方计算平台

Yi Li, Wei Xu.《PrivPy: General and Scalable Privacy-Preserving Data Mining》

同盾科技同盾志邦知识联邦平台

Hongyu Li, Dan Meng, Hong Wang, Xiaolin Li.

《Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework》
《FedMONN: Meta Operation Neural Network for Secure Federated Aggregation》