聯邦學習頂會論文清單-人工智慧與機器學習(2022.07.16)

語言: CN / TW / HK

為了瞭解聯邦學習在學術前沿的落地情況,小魚整理了在頂會上聯邦學習的論文清單。

本文討論的人工智慧與機器學習領域的頂會主要包括:

  • AAAI(AAAI Conference on Artificial Intelligence)
  • AISTATS(Artificial Intelligence and Statistics)
  • NeurIPS(Annual Conference on Neural Information Processing Systems)
  • ICML(International Conference on Machine Learning)
  • ICLR(International Conference on Learning Representations)

在此感謝微眾銀行 innovation-cat 建立的 Awesome-Federated-Machine-Learning 倉庫,他整理了多個領域聯邦學習的頂會工作。

@白小魚 在其整理工作基礎上有補充和進一步修改。

如下是論文清單:

因為格式原因(知乎的編輯器表格和超連結沒法共存),我比較建議去Github的倉庫去看我整理的論文清單~

Awesome-Federated-Learning-on-Graph-and-Tabular-Data#FL-in-top-ML-Conferences

Title Affiliation Venue Year Materials
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images The Chinese University of Hong Kong; Beihang University AAAI 2022 [Code]
Federated Learning for Face Recognition with Gradient Correction Beijing University of Posts and Telecommunications AAAI 2022
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data university of Southern California AAAI 2022 [Code]
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures Harbin Institute of Technology; Peng Cheng Laboratory AAAI 2022
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network Tianjin University AAAI 2022
Seizing Critical Learning Periods in Federated Learning SUNY-Binghamton University; Louisiana State University AAAI 2022
Coordinating Momenta for Cross-silo Federated Learning University of Pittsburgh AAAI 2022
FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney; University of Washington AAAI 2022 [Code]
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University AAAI 2022
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better The University of Texas at Austin AAAI 2022 [Code]
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition National Taiwan University AAAI 2022 [Code]
SplitFed: When Federated Learning Meets Split Learning CSIRO; Lehigh University AAAI 2022 [Code]
Efficient Device Scheduling with Multi-Job Federated Learning Soochow University; Baidu AAAI 2022
Implicit Gradient Alignment in Distributed and Federated Learning IIT Kanpur; EPFL AAAI 2022
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies IBM Research; Wichita State University AAAI 2022
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating Xidian University; JD Tech AAAI 2021 video
FedRec++: Lossless Federated Recommendation with Explicit Feedback Shenzhen University AAAI 2021 video
Federated Multi-Armed Bandits University of Virginia AAAI 2021 [Code] video
On the Convergence of Communication-Efficient Local SGD for Federated Learning Temple University; University of Pittsburgh AAAI 2021 video
FLAME: Differentially Private Federated Learning in the Shuffle Model Renmin University of China; Kyoto University AAAI 2021 video [Code]
Toward Understanding the Influence of Individual Clients in Federated Learning Shanghai Jiao Tong University; The University of Texas at Dallas AAAI 2021 video
Provably Secure Federated Learning against Malicious Clients Duke University AAAI 2021 video slides
Personalized Cross-Silo Federated Learning on Non-IID Data Simon Fraser University; McMaster University AAAI 2021 video
Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation Cornell University AAAI 2021 [Code] video
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning University of Nevada; IBM Research AAAI 2021 video
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning IIT Bombay; IBM Research AAAI 2021 video Supplementary
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models The Chinese University of Hong Kong; Arizona State University AAAI 2021 video [Code]
Adressing Class Imbalance in Federated Learning Northwestern University AAAI 2021 video [Code]
Defending against Backdoors in Federated Learning with Robust Learning Rate The University of Texas at Dallas AAAI 2021 video [Code]
Free-rider Attacks on Model Aggregation in Federated Learning Accenture Labs AISTAT 2021 video Supplementary
Federated f-differential privacy University of Pennsylvania AISTAT 2021 [Code] video Supplementary
Federated learning with compression: Unified analysis and sharp guarantees The Pennsylvania State University; The University of Texas at Austin AISTAT 2021 [Code] video Supplementary
Shuffled Model of Differential Privacy in Federated Learning UCLA; Google AISTAT 2021 video Supplementary
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning Google AISTAT 2021 video Supplementary
Federated Multi-armed Bandits with Personalization University of Virginia; The Pennsylvania State University AISTAT 2021 [Code] video Supplementary
Towards Flexible Device Participation in Federated Learning CMU; Sun Yat-Sen University AISTAT 2021 video Supplementary
Practical Federated Gradient Boosting Decision Trees National University of Singapore; The University of Western Australia AAAI 2020 [Code]
Federated Learning for Vision-and-Language Grounding Problems Peking University; Tencent AAAI 2020
Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework Beihang University AAAI 2020
Federated Patient Hashing Cornell University AAAI 2020
Robust Federated Learning via Collaborative Machine Teaching Symantec Research Labs; KAUST AAAI 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization UC Santa Barbara; UT Austin AISTAT 2020 video Supplementary
How To Backdoor Federated Learning Cornell Tech AISTAT 2020 video [Code] Supplementary
Federated Heavy Hitters Discovery with Differential Privacy RPI; Google AISTAT 2020 video Supplementary
Title Affiliation Venue Year Materials
Fast Composite Optimization and Statistical Recovery in Federated Learning Shanghai Jiao Tong University ICML 2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning New York University ICML 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Stanford University; Google Research ICML 2022 code slides
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation Stanford University; Google Research ICML 2022
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training University of Science and Technology of China ICML 2022 code
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning University of Oulu ICML 2022 code
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning University of Cambridge ICML 2022 slides
Accelerated Federated Learning with Decoupled Adaptive Optimization Auburn University ICML 2022
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling Geogia Institute of Technology ICML 2022
Multi-Level Branched Regularization for Federated Learning Seoul National University ICML 2022 HomePage
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale University of Michigan ICML 2022 code
Federated Learning with Positive and Unlabeled Data Xi’an Jiaotong University ICML 2022
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning Shanghai Jiao Tong University ICML 2022 code
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering University of Michigan ICML 2022 code
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring University of Science and Technology of China ICML 2022 slides
Architecture Agnostic Federated Learning for Neural Networks The University of Texas at Austin ICML 2022
Personalized Federated Learning through Local Memorization Inria ICML 2022 code
Proximal and Federated Random Reshuffling KAUST ICML 2022 code
Federated Learning with Partial Model Personalization University of Washington ICML 2022 code
Generalized Federated Learning via Sharpness Aware Minimization University of South Florida ICML 2022
FedNL: Making Newton-Type Methods Applicable to Federated Learning KAUST ICML 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Carnegie Mellon University ICML 2022 slides
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Hong Kong Baptist University ICML 2022 code
FedNest: Federated Bilevel, Minimax, and Compositional Optimization University of Michigan ICML 2022 code
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning VMware Research ICML 2022 code
Communication-Efficient Adaptive Federated Learning Pennsylvania State University ICML 2022
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training CISPA Helmholz Center for Information Security ICML 2022 code
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification University of Maryland ICML 2022 code
Anarchic Federated Learning The Ohio State University ICML 2022
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning Nankai University ICML 2022 code
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization KAIST ICML 2022
Neural Tangent Kernel Empowered Federated Learning NC State University ICML 2022 code
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy University of Minnesota ICML 2022
Personalized Federated Learning via Variational Bayesian Inference Chinese Academy of Sciences ICML 2022
Federated Learning with Label Distribution Skew via Logits Calibration Zhejiang University ICML 2022
Neurotoxin: Durable Backdoors in Federated Learning Southeast University;Princeton University ICML 2022 code
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems Michigan State University ICML 2022
Bayesian Framework for Gradient Leakage ETH Zurich ICLR 2022 [Code]
Federated Learning from only unlabeled data with class-conditional-sharing clients The University of Tokyo; The Chinese University of Hong Kong ICLR 2022 [Code]
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning Carnegie Mellon University; University of Illinois at Urbana-Champaign; University of Washington ICLR 2022
Acceleration of Federated Learning with Alleviated Forgetting in Local Training Tsinghua University ICLR 2022 [Code]
FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning POSTECH ICLR 2022 [Code]
An Agnostic Approach to Federated Learning with Class Imbalance University of Pennsylvania ICLR 2022 [Code]
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization Michigan State University; The University of Texas at Austin ICLR 2022 [Code]
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models University of Maryland; New York University ICLR 2022 [Code] (Minimum) [Code] (Comprehensive)
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity University of Cambridge; University of Oxford ICLR 2022
Diverse Client Selection for Federated Learning via Submodular Maximization Intel; Carnegie Mellon University ICLR 2022 [Code]
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? Purdue University ICLR 2022 [Code]
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions University of Maryland; Google ICLR 2022 [Code]
Towards Model Agnostic Federated Learning Using Knowledge Distillation EPFL ICLR 2022
Divergence-aware Federated Self-Supervised Learning Nanyang Technological University; SenseTime ICLR 2022
What Do We Mean by Generalization in Federated Learning? Stanford University; Google ICLR 2022 [Code]
FedBABU: Toward Enhanced Representation for Federated Image Classification KAIST ICLR 2022 [Code]
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing EPFL ICLR 2022 [Code]
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters Aibee ICLR Spotlight 2022 Homepage
Hybrid Local SGD for Federated Learning with Heterogeneous Communications University of Texas; Pennsylvania State University ICLR 2022
On Bridging Generic and Personalized Federated Learning for Image Classification The Ohio State University ICLR 2022 [Code]
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond KAIST; MIT ICLR 2022
Federated Learning Based on Dynamic Regularization Boston University; ARM ICLR 2021
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning The Ohio State University ICLR 2021
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients Duke University ICLR 2021 [Code]
FedMix: Approximation of Mixup under Mean Augmented Federated Learning KAIST ICLR 2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms CMU; Google ICLR 2021 [Code]
Adaptive Federated Optimization Google ICLR 2021 [Code]
Personalized Federated Learning with First Order Model Optimization Stanford University; NVIDIA ICLR 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Princeton University ICLR 2021 [Code]
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning The Ohio State University ICLR 2021
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning KAIST ICLR 2021 [Code]
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix Harvard University ICML 2021 video [Code]
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis Peking University; Princeton University ICML 2021 video
Personalized Federated Learning using Hypernetworks Bar-Ilan University; NVIDIA ICML 2021 [Code] HomePage video
Federated Composite Optimization Stanford University; Google ICML 2021 [Code] video slides
Exploiting Shared Representations for Personalized Federated Learning University of Texas at Austin; University of Pennsylvania ICML 2021 [Code] video
Data-Free Knowledge Distillation for Heterogeneous Federated Learning Michigan State University ICML 2021 [Code] video
Federated Continual Learning with Weighted Inter-client Transfer KAIST ICML 2021 [Code] video
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity The University of Iowa ICML 2021 video
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning The University of Tokyo ICML 2021 video
Federated Learning of User Verification Models Without Sharing Embeddings Qualcomm ICML 2021 video
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning Accenture ICML 2021 [Code] video
Ditto: Fair and Robust Federated Learning Through Personalization CMU; Facebook AI ICML 2021 [Code] video
Heterogeneity for the Win: One-Shot Federated Clustering CMU ICML 2021 video
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Google ICML 2021 video
Debiasing Model Updates for Improving Personalized Federated Training Boston University; Arm ICML 2021 video
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning Toyota; Berkeley; Cornell University ICML 2021 [Code] video
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks UIUC; IBM ICML 2021 [Code] video
Federated Learning under Arbitrary Communication Patterns Indiana University; Amazon ICML 2021 video
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries KAIST NeurIPS 2021 HomePage
CAFE: Catastrophic Data Leakage in Vertical Federated Learning Rensselaer Polytechnic Institute; IBM Research NeurIPS 2021 [Code] HomePage
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee NUS NeurIPS 2021 [Code] HomePage
Optimality and Stability in Federated Learning: A Game-theoretic Approach Cornell University NeurIPS 2021 [Code] HomePage
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning UCLA NeurIPS 2021 HomePage
The Skellam Mechanism for Differentially Private Federated Learning Google Research; CMU NeurIPS 2021 HomePage
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data NUS; Huawei NeurIPS 2021 HomePage
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning University of Minnesota NeurIPS 2021 HomePage
Subgraph Federated Learning with Missing Neighbor Generation Emory University; University of British Columbia; Lehigh University NeurIPS 2021 HomePage
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Princeton University NeurIPS 2021 HomePage
Personalized Federated Learning With Gaussian Processes Bar-Ilan University NeurIPS 2021 [Code] HomePage
Differentially Private Federated Bayesian Optimization with Distributed Exploration MIT; NUS NeurIPS 2021 [Code] HomePage
Parameterized Knowledge Transfer for Personalized Federated Learning Hong Kong Polytechnic University; NeurIPS 2021 HomePage
Federated Reconstruction: Partially Local Federated Learning Google Research NeurIPS 2021 HomePage
Fast Federated Learning in the Presence of Arbitrary Device Unavailability Tsinghua University; Princeton University; MIT NeurIPS 2021 [Code] HomePage
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective Duke University; Accenture Labs NeurIPS 2021 [Code] HomePage
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout KAUST; Samsung AI Center NeurIPS 2021 HomePage
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients University of Pennsylvania NeurIPS 2021 HomePage
Federated Multi-Task Learning under a Mixture of Distributions INRIA; Accenture Labs NeurIPS 2021 [Code] HomePage
Federated Graph Classification over Non-IID Graphs Emory University NeurIPS 2021 HomePage
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing CMU; Hewlett Packard Enterprise NeurIPS 2021 [Code] HomePage
On Large-Cohort Training for Federated Learning Google; CMU NeurIPS 2021 [Code] HomePage
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning KAUST; Columbia University; University of Central Florida NeurIPS 2021 [Code] HomePage
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization Huawei NeurIPS 2021 HomePage
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis KAIST NeurIPS 2021 HomePage
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning Tsinghua University; Alibaba; Weill Cornell Medicine NeurIPS 2021 [Code] HomePage
Federated Linear Contextual Bandits The Pennsylvania State University; Facebook; University of Virginia NeurIPS 2021 HomePage
Few-Round Learning for Federated Learning KAIST NeurIPS 2021 HomePage
Breaking the centralized barrier for cross-device federated learning EPFL; Google Research NeurIPS 2021 [Code] HomePage Video
Federated-EM with heterogeneity mitigation and variance reduction Ecole Polytechnique; Google Research NeurIPS 2021 HomePage
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning MIT; Amazon; Google NeurIPS 2021 HomePage
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization University of North Carolina at Chapel Hill; IBM Research NeurIPS 2021 [Code] HomePage
Gradient Inversion with Generative Image Prior Pohang University of Science and Technology; University of Wisconsin-Madison; University of Washington NeurIPS 2021 [Code] HomePage
Federated Adversarial Domain Adaptation Boston University; Columbia University; Rutgers University ICLR 2020
DBA: Distributed Backdoor Attacks against Federated Learning Zhejiang University; IBM Research ICLR 2020 [Code]
Fair Resource Allocation in Federated Learning CMU; Facebook AI ICLR 2020 [Code]
Federated Learning with Matched Averaging University of Wisconsin-Madison; IBM Research ICLR 2020 [Code]
Differentially Private Meta-Learning CMU ICLR 2020
Generative Models for Effective ML on Private, Decentralized Datasets Google ICLR 2020 [Code]
On the Convergence of FedAvg on Non-IID Data Peking University ICLR 2020 [Code]
FedBoost: A Communication-Efficient Algorithm for Federated Learning Google ICML 2020 Video
FetchSGD: Communication-Efficient Federated Learning with Sketching UC Berkeley; Johns Hopkins University; Amazon ICML 2020 Video [Code]
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning EPFL; Google ICML 2020 Video
Federated Learning with Only Positive Labels Google ICML 2020 Video
From Local SGD to Local Fixed-Point Methods for Federated Learning Moscow Institute of Physics and Technology; KAUST ICML 2020 Slide Video
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization KAUST ICML 2020 Slide Video
Differentially-Private Federated Linear Bandits MIT NeurIPS 2020 [Code]
Federated Principal Component Analysis University of Cambridge; Quine Technologies NeurIPS 2020 [Code]
FedSplit: an algorithmic framework for fast federated optimization UC Berkeley NeurIPS 2020
Federated Bayesian Optimization via Thompson Sampling NUS; MIT NeurIPS 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning KAUST NeurIPS 2020
Robust Federated Learning: The Case of Affine Distribution Shifts UC Santa Barbara; MIT NeurIPS 2020
An Efficient Framework for Clustered Federated Learning UC Berkeley; DeepMind NeurIPS 2020 [Code]
Distributionally Robust Federated Averaging Pennsylvania State University NeurIPS 2020 [Code]
Personalized Federated Learning with Moreau Envelopes The University of Sydney NeurIPS 2020 [Code]
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach MIT; UT Austin NeurIPS 2020
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge University of Southern California NeurIPS 2020 [Code]
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization CMU; Princeton University NeurIPS 2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning University of Wisconsin-Madison NeurIPS 2020
Federated Accelerated Stochastic Gradient Descent Stanford University NeurIPS 2020 [Code]
Inverting Gradients - How easy is it to break privacy in federated learning? University of Siegen NeurIPS 2020 [Code]
Ensemble Distillation for Robust Model Fusion in Federated Learning EPFL NeurIPS 2020
Throughput-Optimal Topology Design for Cross-Silo Federated Learning INRIA NeurIPS 2020 [Code]
Bayesian Nonparametric Federated Learning of Neural Networks IBM ICML 2019 [Code]
Analyzing Federated Learning through an Adversarial Lens Princeton University; IBM ICML 2019 [Code]
Agnostic Federated Learning Google ICML 2019
cpSGD: Communication-efficient and differentially-private distributed SGD Princeton University; Google NeurIPS 2018
Federated Multi-Task Learning Stanford; USC; CMU NeurIPS 2018 [Code]