人工智能（Artificial Intelligence, AI）进入以深度学习为主导的大数据时代，基于大数据的机器学习既推动了AI的蓬勃发展，也带来了一系列安全隐患。这些隐患来源于深度学习本身的学习机制，无论是在它的模型建造（训练）阶段，还是在模型推理和使用阶段。这些安全隐患如果被有意或无意地滥用，后果将十分严重。
联邦学习是一种 隐私保护、数据本地存储与计算 的机器学习算法。
- 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
- 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
- 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
- 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
- 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)
- 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
- 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)
- 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?
- Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning （NIPS 2019 Workshop)
- Quantification of the Leakage in Federated Learning
- 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
- 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)
- Federated Meta-Learning with Fast Convergence and Efficient Communication
- Federated Meta-Learning for Recommendation
- Adaptive Gradient-Based Meta-Learning Methods
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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.
- 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)
- 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
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- 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
- 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
- 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
- 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
- Federated Learning Approach for Mobile Packet Classification
- Federated Learning for Ranking Browser History Suggestions (NIPS 2019 Workshop)
- 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)
- 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
- 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)
- 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
- Turbofan POC: Predictive Maintenance of Turbofan Engines using Federated Learning
- Turbofan Tycoon Simulation by Cloudera/FastForwardLabs
- Firefox Search Bar
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》
Yi Li, Wei Xu.《PrivPy: General and Scalable Privacy-Preserving Data Mining》
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》
百度 MesaTEE 安全计算平台
Tongxin Li, Yu Ding, Yulong Zhang, Tao Wei.《gbdt-rs: Fast and Trustworthy Gradient Boosting Decision Tree》
矩阵元 Rosetta 隐私开源框架
百度 PaddlePaddle 开源联邦学习框架
蚂蚁区块链科技 蚂蚁链摩斯安全计算平台
阿里云 DataTrust 隐私增强计算平台
《FedVision: An Online Visual Object Detection Platform Powered by Federated Learning》
《BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning》
《Abnormal Client Behavior Detection in Federated Learning》
《Federated machine learning: Concept and applications》
《Failure Prediction in Production Line Based on Federated Learning: An Empirical Study》
Google 提出 Federated Learning. H. Brendan McMahan. Daniel Ramage. Jakub Konečný. Kallista A. Bonawitz. Hubert Eichner.
《Communication-efficient learning of deep networks from decentralized data》
《Federated Learning: Strategies for Improving Communication Efficiency》
《Advances and Open Problems in Federated Learning》
《Towards Federated Learning at Scale: System Design》
《Differentially Private Learning with Adaptive Clipping》
......（更多联邦学习相关文章请自行搜索 Google Scholar）
Antonio Marcedone.
《Practical Secure Aggregation for Federated Learning on User-Held Data》
《Practical Secure Aggregation for Privacy-Preserving Machine Learning》
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov.
《How To Backdoor Federated Learning》
《Differential privacy has disparate impact on model accuracy》
Ziteng Sun.