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Federated Learning Paper in Conferences

Table of Contents

Statistics of the number of papers in top conferences.

Conference Year Numer of Papers
NeurIPS 2021 32
NeurIPS 2020 24
ICLR 2023 46
ICLR 2022 20
ICLR 2021 10
ICML 2022 37
ICML 2021 18
ICML 2021 5
CVPR 2022 19
CVPR 2021 5
ICCV 2021 3
OSDI 2021 1
SoCC 2022 1

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]](FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning) [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]