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Top paper based on federated learning

Posted by Hao Do on September 22, 2023

Conference Top paper based on federated learning

NeurIPS

NeurIPS 2021 (32 Papers)

  • Sageflow: Robust Federated Learning against Both Stragglers and Adversaries [Paper]
  • Catastrophic Data Leakage in Vertical Federated Learning [Paper]
  • Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee [Paper]
  • Optimality and Stability in Federated Learning: A Game-theoretic Approach [Paper]
  • QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning [Paper]
  • The Skellam Mechanism for Differentially Private Federated Learning [Paper]
  • No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [Paper]
  • STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning [Paper]
  • Subgraph Federated Learning with Missing Neighbor Generation [Paper]
  • Evaluating Gradient Inversion Attacks and Defenses in Federated Learning [Paper]
  • Personalized Federated Learning With Gaussian Processes [Paper]
  • Differentially Private Federated Bayesian Optimization with Distributed Exploration [Paper]
  • Parameterized Knowledge Transfer for Personalized Federated Learning [Paper]
  • Federated Reconstruction: Partially Local Federated Learning [Paper]
  • Fast Federated Learning in the Presence of Arbitrary Device Unavailability [Paper]
  • FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout [Paper]
  • FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective [Paper]
  • Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients [Paper]
  • Federated Multi-Task Learning under a Mixture of Distributions [Paper]
  • Federated Graph Classification over Non-IID Graphs [Paper]
  • Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing [Paper]
  • On Large-Cohort Training for Federated Learning [Paper]
  • DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning [Paper]
  • PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization [Paper]
  • Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis [Paper]
  • Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning [Paper]
  • Federated Linear Contextual Bandits [Paper]
  • Few-Round Learning for Federated Learning [Paper]
  • Breaking the centralized barrier for cross-device federated learning [Paper]
  • Federated-EM with heterogeneity mitigation and variance reduction [Paper]
  • Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning [Paper]
  • FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization [Paper]

NeurIPS 2020 (24 Papers)

  • Personalized Federated Learning with Moreau Envelopes [Paper]
  • Lower Bounds and Optimal Algorithms for Personalized Federated Learning [Paper] [KAUST]
  • Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach [Paper] [MIT]
  • Federated Principal Component Analysis [Paper] [Cambridge]
  • FedSplit: an algorithmic framework for fast federated optimization [Paper] [Berkeley]
  • Minibatch vs Local SGD for Heterogeneous Distributed Learning [Paper] [Toyota]
  • Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms [Paper]
  • Throughput-Optimal Topology Design for Cross-Silo Federated Learning [Paper]
  • Distributed Distillation for On-Device Learning [Paper] [Stanford]
  • Ensemble Distillation for Robust Model Fusion in Federated Learning [Paper]
    • Nice experimentation graphs and comparison with FedAvg
  • Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge [Paper] [USC]
  • Federated Accelerated Stochastic Gradient Descent [Paper] [Github] [Stanford]
  • Distributionally Robust Federated Averaging [Paper]
  • An Efficient Framework for Clustered Federated Learning [Paper] [Berkeley]
  • Robust Federated Learning: The Case of Affine Distribution Shifts [Paper] [MIT]
  • Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization [Paper] [CMU]
  • Federated Bayesian Optimization via Thompson Sampling [Paper] [NUS] [MIT]
  • Distributed Newton Can Communicate Less and Resist Byzantine Workers [Paper] [Berkeley]
  • Byzantine Resilient Distributed Multi-Task Learning [Paper]
  • A Scalable Approach for Privacy-Preserving Collaborative Machine Learning [Paper] [USC]
  • Inverting Gradients - How easy is it to break privacy in federated learning? [Paper]
  • Attack of the Tails: Yes, You Really Can Backdoor Federated Learning [Paper]
  • Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks [Paper]
  • Differentially-Private Federated Linear Bandits [Paper] [Slides] [MIT]

NeurIPS 2020 Workshop

  • Can Federated Learning Save The Planet? [Paper]

NeurIPS 2019 Workshop

  • NIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality 1 [Video]
  • NIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality 2 [Video]
  • NIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality 3 [Video]

ICLR

ICLR 2023 (46 Papers)

  • Personalized Federated Learning with Feature Alignment and Classifier Collaboration [Paper]
  • MocoSFL: enabling cross-client collaborative self-supervised learning [Paper]
  • Single-shot General Hyper-parameter Optimization for Federated Learning [Paper]
  • Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated [Paper]
  • FedExP: Speeding up Federated Averaging via Extrapolation [Paper]
  • Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection [Paper]
  • DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity [Paper]
  • Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach [Paper]
  • Federated Learning from Small Datasets [Paper]
  • Machine Unlearning of Federated Clusters [Paper]
  • Federated Neural Bandits [Paper]
  • FedFA: Federated Feature Augmentation [Paper]
  • Better Generative Replay for Continual Federated Learning [Paper]
  • Federated Nearest Neighbor Machine Translation [Paper]
  • Test-Time Robust Personalization for Federated Learning [Paper]
  • DepthFL : Depthwise Federated Learning for Heterogeneous Clients [Paper]
  • Towards Addressing Label Skews in One-Shot Federated Learning [Paper]
  • Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning [Paper]
  • Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation [Paper]
  • SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication [Paper]
  • Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses [Paper]
  • Effective passive membership inference attacks in federated learning against overparameterized models [Paper]
  • FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification [Paper]
  • Multimodal Federated Learning via Contrastive Representation Ensemble [Paper]
  • Faster federated optimization under second-order similarity [Paper]
  • FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy [Paper]
  • The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation [Paper]
  • PerFedMask: Personalized Federated Learning with Optimized Masking Vectors [Paper]
  • EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data [Paper]
  • FedDAR: Federated Domain-Aware Representation Learning [Paper]
  • Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning [Paper]
  • FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning [Paper]
  • Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses [Paper]
  • Combating Exacerbated Heterogeneity for Robust Models in Federated Learning [Paper]
  • Efficient Federated Domain Translation [Paper]
  • On the Importance and Applicability of Pre-Training for Federated Learning [Paper]
  • Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models [Paper]
  • A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy [Paper]
  • Instance-wise Batch Label Restoration via Gradients in Federated Learning [Paper]
  • Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity [Paper]
  • Meta Knowledge Condensation for Federated Learning [Paper]
  • CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning [Paper]
  • Sparse Random Networks for Communication-Efficient Federated Learning [Paper]
  • Hyperparameter Optimization through Neural Network Partitioning [Paper]
  • Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? [Paper]
  • Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top [Paper]

ICLR 2022 (20 Papers)

Spotlight

  • On Bridging Generic and Personalized Federated Learning for Image Classification [Paper]
  • Hybrid Local SGD for Federated Learning with Heterogeneous Communications [Paper]
  • Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters [Paper]
  • Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing [Paper]

Poster

  • Divergence-aware Federated Self-Supervised Learning [Paper]
  • FedBABU: Toward Enhanced Representation for Federated Image Classification [Paper]
  • What Do We Mean by Generalization in Federated Learning? [Paper]
  • Towards Model Agnostic Federated Learning Using Knowledge Distillation [Paper]
  • Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions [Paper]
  • Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? [Paper]
  • Diverse Client Selection for Federated Learning via Submodular Maximization [Paper]
  • ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity [Paper]
  • Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models [Paper]
  • Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization [Paper]
  • An Agnostic Approach to Federated Learning with Class Imbalance [Paper]
  • FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning [Paper]
  • Acceleration of Federated Learning with Alleviated Forgetting in Local Training [Paper]
  • Reducing the Communication Cost of Federated Learning through Multistage Optimization [Paper]
  • Unsupervised Federated Learning is Possible [Paper]
  • Bayesian Framework for Gradient Leakage [Paper]

ICLR 2021 (10 Papers)

  • Federated Learning Based on Dynamic Regularization [Paper]
  • Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms [Paper]
  • Adaptive Federated Optimization [Paper]
  • Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning [Paper]
  • Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning [Paper]
  • FedBN: Federated Learning on Non-IID Features via Local Batch Normalization [Paper]
  • FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning [Paper]
  • FedMix: Approximation of Mixup under Mean Augmented Federated Learning [Paper]
  • HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients [Paper]
  • Personalized Federated Learning with First Order Model Optimization [Paper]

ICML

ICML 2022 (37 Papers)

  • Fast Composite Optimization and Statistical Recovery in Federated Learning [Paper] [Supplementary]
  • Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning [Paper]
  • The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning [Paper]
  • The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation [Paper]
  • DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training [Paper]
  • FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning [Paper] [Supplementary]
  • DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning [Paper] [Supplementary]
  • Accelerated Federated Learning with Decoupled Adaptive Optimization [Paper] [Supplementary]
  • Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling [Paper]
  • Multi-Level Branched Regularization for Federated Learning [Paper]
  • FedScale: Benchmarking Model and System Performance of Federated Learning at Scale162:11814-11827 [Paper]
  • Federated Learning with Positive and Unlabeled Data [Paper]
  • Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning [Paper]
  • Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering [Paper] [Supplementary]
  • Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring [Paper]
  • Architecture Agnostic Federated Learning for Neural Networks [Paper]
  • Personalized Federated Learning through Local Memorization [Paper]
  • Proximal and Federated Random Reshuffling [Paper]
  • Federated Learning with Partial Model Personalization [Paper]
  • Generalized Federated Learning via Sharpness Aware Minimization [Paper]
  • FedNL: Making Newton-Type Methods Applicable to Federated Learning [Paper] [Supplementary]
  • Federated Minimax Optimization: Improved Convergence Analyses and Algorithms [Paper]
  • Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning [Paper]
  • FedNest: Federated Bilevel, Minimax, and Compositional Optimization [Paper] [Supplementary]
  • EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning [Paper]
  • Communication-Efficient Adaptive Federated Learning [Paper]
  • ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training [Paper]
  • Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification [Paper]
  • Anarchic Federated Learning [Paper]
  • QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning [Paper]
  • Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization [Paper]
  • Neural Tangent Kernel Empowered Federated Learning [Paper]
  • Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy [Paper]
  • Personalized Federated Learning via Variational Bayesian Inference [Paper]
  • Federated Learning with Label Distribution Skew via Logits Calibration [Paper]
  • Neurotoxin: Durable Backdoors in Federated Learning [Paper]
  • Resilient and Communication Efficient Learning for Heterogeneous Federated Systems [Paper]

ICML 2021 (18 Papers)

ICML 2020 (5 Papers)

  • FedBoost: Communication-Efficient Algorithms for Federated Learning [Paper] [ICML20]
  • FetchSGD: Communication-Efficient Federated Learning with Sketching [Paper] [ICML20]
  • Federated Learning with Only Positive Labels [Paper] [Google]
  • SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning [Paper]
  • From Local SGD to Local Fixed-Point Methods for Federated Learning [Paper]

CVPR

CVPR 2022 (18 Papers)

  • Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation [Paper]
  • ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework [Paper]
  • Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning [Paper]
  • FedCorr: Multi-Stage Federated Learning for Label Noise Correction [Paper]
  • Layer-Wised Model Aggregation for Personalized Federated Learning [Paper]
  • Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [Paper]
  • Federated Learning With Position-Aware Neurons [Paper]
  • Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage [Paper]
  • FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction [Paper]
  • RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning [Paper]
  • Learn From Others and Be Yourself in Heterogeneous Federated Learning [Paper]
  • Federated Class-Incremental Learning [Paper]
  • Differentially Private Federated Learning With Local Regularization and Sparsification [Paper]
  • Robust Federated Learning With Noisy and Heterogeneous Clients [Paper]
  • ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning [Paper]
  • FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning [Paper]
  • Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [Paper]
  • CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning [Paper]

CVPR 2021 (5 Papers)

  • Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning [Paper]
  • Model-Contrastive Federated Learning [Paper]
  • FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [Paper]
  • Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective [Paper]
  • Privacy-Preserving Collaborative Learning With Automatic Transformation Search [Paper]

ICCV

ICCV 2021 (3 Papers)

  • Collaborative Unsupervised Visual Representation Learning From Decentralized Data [Paper]
  • Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment [Paper]
  • Ensemble Attention Distillation for Privacy-Preserving Federated Learning [Paper]

KDD

KDD 2020

  • FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems [KDD20]
  • Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data [KDD20]
  • FedCD: Improving Performance in non-IID Federated Learning [KDD20 Workshop]
  • Resource-Constrained Federated Learning with Heterogeneous Labels and Models [KDD2020 Workshop]

KDD 2021

  • FLOP: Federated Learning on Medical Datasets using Partial Networks [Paper]
  • Federated Adversarial Debiasing for Fair and Transferable Representations
  • Fed^2: Feature-Aligned Federated Learning
  • Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling [Paper]
  • FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data [Paper]

ACMMM

ACMMM 2021

  • Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification [Paper] [ACMMM21]

ACMMM 2020

  • Performance Optimization for Federated Person Re-identification via Benchmark Analysis [Paper] [ACMMM20] [Github]

  • Invisible: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages [Paper]

OSDI

OSDI 2021

  • Oort: Efficient Federated Learning via Guided Participant Selection [Paper]

SoCC

SoCC 2022

  • Pisces: Efficient Federated Learning via Guided Asynchronous Training [Paper]

AAAI

AAAI 2021

Accepted Papers

  • Federated Multi-Armed Bandits [Paper]
  • Game of Gradients: Mitigating Irrelevant Clients in Federated Learning [Paper]
  • Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [Paper]
  • Provably Secure Federated Learning against Malicious Clients [Paper]
  • On the Convergence of Communication-Efficient Local SGD for Federated Learning
  • Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating [Paper]
  • FedRec++: Lossless Federated Recommendation with Explicit Feedback [PPT]
  • Communication-Aware Collaborative Learning [Paper]
  • Peer Collaborative Learning for Online Knowledge Distillation [Paper]
  • A Communication Efficient Collaborative Learning Framework for Distributed Features [Paper]
  • Defending Against Backdoors in Federated Learning with Robust Learning Rate [Paper]
  • FLAME: Differentially Private Federated Learning in the Shuffle Model [Paper]
  • Toward Understanding the Influence of Individual Clients in Federated Learning [Paper]
  • Personalized Cross-Silo Federated Learning on Non-IID Data [Paper]
  • Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation [Paper]
  • Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models [Paper]
  • Addressing Class Imbalance in Federated Learning [Paper]

IJCAI

IJCAI 2021

Workshop on Federated Learning for User Privacy and Data Confidentiality

  • Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos [Paper]
  • A Multi-player Game for Studying Federated Learning Incentive Schemes [Paper] [IJCAI 2021 Demonstration Track]
  • Federated Meta-Learning for Fraudulent Credit Card Detection [Paper] [IJCAI 2021 Special Track on FinTech]
  • Collaborative Fairness in Federated Learning [Paper] [IJCAI 2021 Workshop Best Paper]
  • FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning [Paper] [IJCAI 2021 Workshop Best Student Paper]
  • Federated Learning with Diversified Preference for Humor Recognition [Paper] [IJCAI 2021 Workshop Best Application Paper]
  • Heterogeneous Data-Aware Federated Learning [Paper] [IJCAI 2021 Workshop]
  • Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention [Paper] [IJCAI 2021 Workshop]
  • FedMVT: Semi-supervised Vertical Federated Learning with MultiView Training [Paper] [IJCAI 2021 Workshop]
  • FOCUS: Dealing with Label Quality Disparity in Federated Learning [Paper] [IJCAI 2021 Workshop]
  • Fed-Focal Loss for imbalanced data classification in Federated Learning [Paper] [IJCAI 2021 Workshop]
  • Threats to Federated Learning: A Survey [Paper] [IJCAI 2021 Workshop]
  • Asymmetrical Vertical Federated Learning [Paper] [IJCAI 2021 Workshop]
  • Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations [Paper] [IJCAI 2021 Workshop]
  • Achieving Differential Privacy in Vertically Partitioned Multiparty Learning [Paper] [IJCAI 2021 Workshop]
  • Privacy Threats Against Federated Matrix Factorization [Paper] [IJCAI 2021 Workshop]
  • TF-SProD: Time Fading based Sensitive Pattern Hiding in Progressive Data [Paper] [IJCAI 2021 Workshop]

Federated Learning Paper in Journal

IEEE Transactions on Parallel and Distributed Systems (TPDS)

  • Biscotti: A Blockchain System for Private and Secure Federated Learning [Paper]
  • Mutual Information Driven Federated Learning [Paper]
  • Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems [Paper]
  • Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems [Paper] [Github]
  • Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity [Paper]
  • An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee [Paper]

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

  • Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data [Paper]

IEEE Internet of Things (IoT)

  • Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing [Paper]
  • Communication-Efficient Federated Learning and Permissioned Blockchain for Digital Twin Edge Networks [Paper]
  • CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes [Paper]
  • Privacy-Preserving Federated Learning in Fog Computing [Paper]
  • FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning [Paper]
  • Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching [Paper]
  • FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things [Paper]
  • Personalized Federated Learning With Differential Privacy [Paper]
  • Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach [Paper]
  • Federated Sensing: Edge-Cloud Elastic Collaborative Learning for Intelligent Sensing [Paper]
  • PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems [Paper]

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