refresh ICML 2023 information

youngfish42 · Aug 4, 2023 · 2c79942 · 2c79942
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@@ -384,44 +384,44 @@ Federated Learning papers accepted by top ML(machine learning) conference and jo
 | Secure Federated Correlation Test and Entropy Estimation | CMU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=ICk7GJ1awE)] [[PDF](https://arxiv.org/abs/2105.14618)] |
 | Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | JLU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=JC05k0E2EM)] [[CODE](https://github.com/YamingGuo98/FedIIR)] |
 | Personalized Federated Learning under Mixture of Distributions | UCLA | ICML | 2023 | [[PUB](https://openreview.net/forum?id=nmVOTsQGR9)] [[PDF](https://arxiv.org/abs/2305.01068)] [[CODE](https://github.com/zshuai8/FedGMM_ICML2023)] |
-| FedDisco: Federated Learning with Discrepancy-Aware Collaboration |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=cHJ1VuZorx)] |
-| Anchor Sampling for Federated Learning with Partial Client Participation |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Ht9r3P6Lts)] [[CODE](https://github.com/harliwu/fedamd)] |
-| Private Federated Learning with Autotuned Compression |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=y8qAZhWbNs)] |
-| Fast Federated Machine Unlearning with Nonlinear Functional Theory |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=6wQKmKiDHw)] |
-| On the Convergence of Federated Averaging with Cyclic Client Participation |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=d8LTNXt97w)] |
-| Revisiting Weighted Aggregation in Federated Learning with Neural Networks |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=FuDAjnWhrQ)] [[CODE](https://github.com/zexilee/icml-2023-fedlaw)] |
-| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=WfI3I8OjHS)] |
-| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=iASUTBGw07)] |
-| Flash: Concept Drift Adaptation in Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=q5RHsg6VRw)] |
-| DoCoFL: Downlink Compression for Cross-Device Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=VxKr51JjWC)] |
-| FeDXL: Provable Federated Learning for Deep X-Risk Optimization |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=C7fNCYdptO)] [[CODE](https://github.com/optimization-ai/icml2023_fedxl)] |
-| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=AMuNQEUmGr)] |
-| Personalized Federated Learning with Inferred Collaboration Graphs |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=33fj5Ph3ot)] |
-| Optimizing the Collaboration Structure in Cross-Silo Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=rnNBSMOWvA)] [[CODE](https://github.com/baowenxuan/fedcollab)] |
-| TabLeak: Tabular Data Leakage in Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=mRiDy4qGwB)] |
-| FedCR:  Personalized Federated Learning Based on Across-Client Common  Representation with Conditional Mutual Information Regularization |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=YDC5jTS3LR)] |
-| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=NcbY2UOfko)] |
-| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Otdp5SGQMr)] |
-| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=pRsJIVcjxD)] |
-| Improving the Model Consistency of Decentralized Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=fn2NFlYLBL)] |
-| Efficient Personalized Federated Learning via Sparse Model-Adaptation |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=ieSN7Xyo8g)] [[CODE](https://github.com/alibaba/federatedscope)] |
-| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=CBLDv6SFMn)] |
-| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=2CiaH2Tq4G)] |
-| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=HtHFnHrZXu)] [[CODE](https://github.com/ybdai7/chameleon-durable-backdoor)] |
-| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=7aqVcrXjxa)] |
-| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=nDKoVwNjMH)] [[CODE](https://github.com/lins-lab/fedbr)] |
-| Towards Unbiased Training in Federated Open-world Semi-supervised Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=gHfybro5Sj)] |
-| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Ai1TyAjZt9)] |
-| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Kz0IODB2kj)] [[CODE](https://github.com/junyizhu-ai/surrogate_model_extension)] |
-| Fair yet Asymptotically Equal Collaborative Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=5VhltFPSO8)] |
-| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=uIzkbJgyqc)] |
-| Adversarial Collaborative Learning on Non-IID Features |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=DVF7gEQQf7)] |
-| XTab: Cross-table Pretraining for Tabular Transformers |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=uGORNDmIdr)] |
-| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=a0kGwNUwil)] |
-| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=3DI6Kmw81p)] |
-| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=L8iWCxzwl1)] |
-| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=eqTWOzheZT)] |
-| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm |  | ICML | 2023 | [[PUB](https://openreview.net/forum?id=iAgQfF3atY)] |
+| FedDisco: Federated Learning with Discrepancy-Aware Collaboration | SJTU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=cHJ1VuZorx)] [PDF](https://arxiv.org/abs/2305.19229) [CODE](https://github.com/MediaBrain-SJTU/FedDisco) |
+| Anchor Sampling for Federated Learning with Partial Client Participation | Purdue University | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Ht9r3P6Lts)] [PDF](https://arxiv.org/abs/2206.05891) [[CODE](https://github.com/harliwu/fedamd)] |
+| Private Federated Learning with Autotuned Compression | JHU; Google | ICML | 2023 | [[PUB](https://openreview.net/forum?id=y8qAZhWbNs)] [PDF](https://arxiv.org/abs/2307.10999) |
+| Fast Federated Machine Unlearning with Nonlinear Functional Theory | Auburn University | ICML | 2023 | [[PUB](https://openreview.net/forum?id=6wQKmKiDHw)] |
+| On the Convergence of Federated Averaging with Cyclic Client Participation | CMU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=d8LTNXt97w)] [PDF](https://arxiv.org/abs/2302.03109) |
+| Revisiting Weighted Aggregation in Federated Learning with Neural Networks | ZJU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=FuDAjnWhrQ)] [PDF](https://arxiv.org/abs/2302.10911) [[CODE](https://github.com/zexilee/icml-2023-fedlaw)] |
+| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond | CMU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=WfI3I8OjHS)] [PDF](https://arxiv.org/abs/2305.10697) [SLIDES](https://icml.cc/media/icml-2023/Slides/24679_ljO6pDE.pdf) |
+| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning | UESTC; NTU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=iASUTBGw07)] |
+| Flash: Concept Drift Adaptation in Federated Learning | University of Massachusetts | ICML | 2023 | [[PUB](https://openreview.net/forum?id=q5RHsg6VRw)] |
+| DoCoFL: Downlink Compression for Cross-Device Federated Learning | VMware Research; Technion | ICML | 2023 | [[PUB](https://openreview.net/forum?id=VxKr51JjWC)] [PDF](https://arxiv.org/abs/2302.00543) |
+| FeDXL: Provable Federated Learning for Deep X-Risk Optimization | Texas A&M University | ICML | 2023 | [[PUB](https://openreview.net/forum?id=C7fNCYdptO)] [PDF](https://arxiv.org/abs/2210.14396) [[CODE](https://github.com/optimization-ai/icml2023_fedxl)] |
+| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | HIT | ICML | 2023 | [[PUB](https://openreview.net/forum?id=AMuNQEUmGr)] [CODE](https://github.com/Hypervoyager/PFL) |
+| Personalized Federated Learning with Inferred Collaboration Graphs | SJTU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=33fj5Ph3ot)] [CODE](https://github.com/MediaBrain-SJTU/pFedGraph) |
+| Optimizing the Collaboration Structure in Cross-Silo Federated Learning | UIUC | ICML | 2023 | [[PUB](https://openreview.net/forum?id=rnNBSMOWvA)] [PDF](https://arxiv.org/abs/2306.06508) [[CODE](https://github.com/baowenxuan/fedcollab)] [SLIDES](https://icml.cc/media/icml-2023/Slides/23569.pdf) |
+| TabLeak: Tabular Data Leakage in Federated Learning | ETH Zurich | ICML | 2023 | [[PUB](https://openreview.net/forum?id=mRiDy4qGwB)] [PDF](https://arxiv.org/abs/2210.01785) [CODE](https://github.com/eth-sri/tableak) |
+| FedCR:  Personalized Federated Learning Based on Across-Client Common  Representation with Conditional Mutual Information Regularization | SJTU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=YDC5jTS3LR)] [CODE](https://github.com/haozzh/FedCR) |
+| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction | Duke University | ICML | 2023 | [[PUB](https://openreview.net/forum?id=NcbY2UOfko)] [PDF](https://arxiv.org/abs/2209.15245) |
+| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Meta AI | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Otdp5SGQMr)] [PDF](https://arxiv.org/abs/2211.03942) [CODE](https://github.com/facebookresearch/dp_compression) |
+| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning | Owkin Inc. | ICML | 2023 | [[PUB](https://openreview.net/forum?id=pRsJIVcjxD)] [PDF](https://arxiv.org/abs/2306.07644) [CODE](https://github.com/owkin/sratta) |
+| Improving the Model Consistency of Decentralized Federated Learning | THU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=fn2NFlYLBL)] [PDF](https://arxiv.org/abs/2302.04083) |
+| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Alibaba Group | ICML | 2023 | [[PUB](https://openreview.net/forum?id=ieSN7Xyo8g)] [PDF](https://arxiv.org/abs/2305.02776) [[CODE](https://github.com/alibaba/federatedscope)] [CODE](https://github.com/yxdyc/pfedgate) |
+| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Univ. Lille | ICML | 2023 | [[PUB](https://openreview.net/forum?id=CBLDv6SFMn)] [PDF](https://arxiv.org/abs/2302.12559) [CODE](https://github.com/totilas/padadmm) |
+| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning | TUD | ICML | 2023 | [[PUB](https://openreview.net/forum?id=2CiaH2Tq4G)] [CODE](https://github.com/chaoyitud/LeadFL) |
+| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | HKUST | ICML | 2023 | [[PUB](https://openreview.net/forum?id=HtHFnHrZXu)] [PDF](https://arxiv.org/abs/2304.12961) [[CODE](https://github.com/ybdai7/chameleon-durable-backdoor)] |
+| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models | HKUST | ICML | 2023 | [[PUB](https://openreview.net/forum?id=7aqVcrXjxa)] [PDF](https://arxiv.org/abs/2304.13407) |
+| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | ICML | 2023 | [[PUB](https://openreview.net/forum?id=nDKoVwNjMH)] [PDF](https://arxiv.org/abs/2205.13462) [[CODE](https://github.com/lins-lab/fedbr)] |
+| Towards Unbiased Training in Federated Open-world Semi-supervised Learning | PolyU | ICML | 2023 | [[PUB](https://openreview.net/forum?id=gHfybro5Sj)] [PDF](https://arxiv.org/abs/2305.00771) [SLIDES](https://icml.cc/media/icml-2023/Slides/25109.pdf) |
+| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis | Georgia Tech; Meta AI | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Ai1TyAjZt9)] [PDF](https://arxiv.org/abs/2209.05578) |
+| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Leuven | ICML | 2023 | [[PUB](https://openreview.net/forum?id=Kz0IODB2kj)] [PDF](https://arxiv.org/abs/2306.00127) [[CODE](https://github.com/junyizhu-ai/surrogate_model_extension)] |
+| Fair yet Asymptotically Equal Collaborative Learning | NUS | ICML | 2023 | [[PUB](https://openreview.net/forum?id=5VhltFPSO8)] [PDF](https://arxiv.org/abs/2306.05764) [CODE](https://github.com/xqlin98/Fair-yet-Equal-CML) |
+| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability | Adobe Research | ICML | 2023 | [[PUB](https://openreview.net/forum?id=uIzkbJgyqc)] [PDF](https://arxiv.org/abs/2210.08371) |
+| Adversarial Collaborative Learning on Non-IID Features | UC Berkeley; NUS | ICML | 2023 | [[PUB](https://openreview.net/forum?id=DVF7gEQQf7)] |
+| XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; AWS | ICML | 2023 | [[PUB](https://openreview.net/forum?id=uGORNDmIdr)] [PDF](https://arxiv.org/abs/2305.06090) [CODE](https://github.com/bingzhaozhu/xtab) |
+| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [[PUB](https://openreview.net/forum?id=a0kGwNUwil)] |
+| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province; ZJU; Sony Al | ICML | 2023 | [[PUB](https://openreview.net/forum?id=3DI6Kmw81p)] [PDF](https://arxiv.org/abs/2302.06079) [CODE](https://github.com/YuchenLiu-a/byzantine-gas) |
+| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning | Rensselaer Polytechnic Institute | ICML | 2023 | [[PUB](https://openreview.net/forum?id=L8iWCxzwl1)] [PDF](https://arxiv.org/abs/2305.02219) |
+| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | University of Minnesota | ICML | 2023 | [[PUB](https://openreview.net/forum?id=eqTWOzheZT)] |
+| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | University of Chicago | ICML | 2023 | [[PUB](https://openreview.net/forum?id=iAgQfF3atY)] [PDF](https://arxiv.org/abs/2306.02543) [CODE](https://github.com/boxinz17/data-market-via-adaptive-sampling) |
 | Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [[PUB](https://link.springer.com/article/10.1007/s10994-022-06292-8)] |
 | FedLab: A Flexible Federated Learning Framework :fire: | UESTC; Peng Cheng Lab | JMLR | 2023 | [[PUB](https://jmlr.org/papers/v24/22-0440.html)] [[PDF](https://arxiv.org/abs/2107.11621)] [[CODE](https://github.com/SMILELab-FL/FedLab)] |
 | Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning | TAMU | JMLR | 2023 | [[PUB](https://jmlr.org/papers/v24/21-1301.html)] [[PDF](https://arxiv.org/abs/2106.04911)] [[CODE](https://github.com/bokun-wang/moml)] |