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[FedBN 2019, SiloBN 2020] Xiaoxiao Li, Meirui JIANG, Xiaofei Zhang, Michael Kamp, and Qi Dou. FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. ICLR 2021. URL: https://openreview.net/pdf?id=6YEQUn0QICG Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, Eric W. Tramel. Siloed Federated Learning for Multi-centric Histopathology Datasets. MICCAI Workshop on Domain Adaptation and Representation Transfer 2020. URL: https://arxiv.org/abs/2008.07424
[FedProx 2018] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated Optimization in Heterogeneous Networks. Adaptive & Multitask Learning Workshop. URL: https://openreview.net/pdf?id=SkgwE5Ss3N
[FedOpt 2021] Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, H. Brendan McMahan. Adaptive Federated Optimization. ICLR 2021. URL: https://openreview.net/pdf?id=LkFG3lB13U5
[SCAFFOLD 2020] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh. SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML 2020. URL: https://arxiv.org/abs/1910.06378
[Moon 2021] Qinbin Li, Bingsheng He, and Dawn Song. Model-Contrastive Federated Learning. CVPR 2021. URL: https://arxiv.org/abs/2103.16257
[FedExP 2023] Divyansh Jhunjhunwala, Shiqiang Wang, and Gauri Joshi. FedExP: Speeding Up Federated Averaging via Extrapolation. ICLR 2023. URL: https://arxiv.org/abs/2301.09604
[Ditto 2021] Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. Ditto: Fair and Robust Federated Learning Through Personalization. ICML 2021. URL: https://arxiv.org/abs/2012.04221
[APFL 2020] Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi. Adaptive Personalized Federated Learning. arXiv 2020. URL: https://arxiv.org/abs/2003.13461
[FedRep 2021] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Exploiting shared representations for personalized federated learning. ICML 2021. URL: https://arxiv.org/abs/2102.07078
[FedPer 2019] Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. Federated learning with personalization layers. arXiv 2019. URL:https://arxiv.org/abs/1912.00818
[FedNova 2020] Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. NeurIPS 2020. URL: https://arxiv.org/abs/2007.07481
[pFedMe 2020] Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen. Personalized Federated Learning with Moreau Envelopes. NeurIPS 2020. URL: https://arxiv.org/abs/2006.08848
[FedDyn 2021] Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. Federated Learning with Dynamic Regularization. ICLR 2021. URL: https://openreview.net/pdf?id=B7v4QMR6Z9w
[LG-FedAvg 2020] Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B. Allen, Randy P. Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency. Think Locally, Act Globally: Federated Learning with Local and Global Representations. arXiv 2020. URL: https://arxiv.org/abs/2001.01523
[SuPerFed 2022] Seok-Ju Hahn, Minwoo Jeong, and Junghye Lee. Connecting Low-Loss Subspace for Personalized Federated Learning. KDD 2022. URL: https://arxiv.org/abs/2109.07628v3
[FedAMP 2021] Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, Yong Zhang. Personalized Cross-Silo Federated Learning on Non-IID Data. AAAI 2021. URL: https://arxiv.org/abs/2007.03797
[FedProto 2022] Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang. FedProto: Federated Prototype Learning across Heterogeneous Clients. AAAI 2022. URL: https://arxiv.org/abs/2105.00243
[FedLC 2022] Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu. Federated Learning with Label Distribution Skew via Logits Calibration. ICML 2022. URL: https://arxiv.org/abs/2209.00189
[FedAVGM 2019] Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown. Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification. arXiv 2019. URL: https://arxiv.org/abs/1909.06335
[FedBABU 2022] Jaehoon Oh, Sangmook Kim, Se-Young Yun. FedBABU: Towards Enhanced Representation for Federated Image Classification. ICLR 2022. URL: https://arxiv.org/abs/2106.06042
[Per-FedAVG 2020] Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar. Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. NeurIPS 2020. URL: https://arxiv.org/abs/2002.07948
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[FedNH 2023] Yutong Dai, Zeyuan Chen, Junnan Li, Shelby Heinecke, Lichao Sun, Ran Xu. Tackling Data Heterogeneity in Federated Learning with Class Prototypes. AAAI 2023. URL: https://arxiv.org/abs/2212.02758
[FedHP 2024] Samuele Fonio, Mirko Polato, Roberto Esposito. Federated Hyperbolic Prototype Learning. ESANN 2024 (to appear)
[FedALA 2023] Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan. FedALA: Adaptive Local Aggregation for Personalized Federated Learning. AAAI 2023. URL: https://arxiv.org/pdf/2212.01197v4
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[FedRS 2021] Xin-Chun Li and De-Chuan Zhan. FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. KDD 2021. URL: https://doi.org/10.1145/3447548.3467254
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[FedROD 2022] Hong-You Chen and Wei-Lun Chao. On Bridging Generic and Personalized Federated Learning for Image Classification. ICLR 2022. URL: https://openreview.net/pdf?id=I1hQbx10Kxn