[隐私计算研习社]
国际表征学习大会（International Conference on Learning Representations，简称 ICLR）是深度学习领域的顶级会议，关注有关深度学习各个方面的前沿研究，在人工智能、统计和数据科学领域以及机器视觉、语音识别、文本理解等重要应用领域中发布了众多极其有影响力的论文。
通过AI技术对 ICLR2023 收录的会议论文进行了分类整理，其中关于联邦学习的论文有34篇。本文将罗列这34篇论文，感兴趣的小伙伴可以访问论文下方的链接阅读原文~
- Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning****
Yujun Shi, Jian Liang, Wenqing Zhang, Vincent Tan, Song Bai
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- Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Jian Xu, Xinyi Tong, Shao-Lun Huang
- The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
Huancheng Chen, Chaining Wang, Haris Vikalo
- Towards Addressing Label Skews in One-Shot Federated Learning
Yiqun Diao, Qinbin Li, Bingsheng He
- FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, Dacheng Tao
- Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses
Xiaolin Hu, Shaojie Li, Yong Liu
- Better Generative Replay for Continual Federated Learning
Daiqing Qi, Handong Zhao, Sheng Li
- Federated Learning from Small Datasets
Michael Kamp, Jonas Fischer, Jilles Vreeken
- FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Aliaksandra Shysheya, John F Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E Turner
- Federated Nearest Neighbor Machine Translation
Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen
- On the Importance and Applicability of Pre-Training for Federated Learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han Wei Shen, Wei-Lun Chao
- SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Marco Bornstein, Tahseen Rabbani, Evan Z Wang, Amrit Bedi, Furong Huang
- Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?
Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake
- FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, Xiangyu Zhang
- Faster federated optimization under second-order similarity
Ahmed Khaled, Chi Jin
- FedFA: Federated Feature Augmentation
Tianfei Zhou, Ender Konukoglu
- FedExP: Speeding up Federated Averaging via Extrapolation
Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
- DepthFL : Depthwise Federated Learning for Heterogeneous Clients
Minjae Kim, Sangyoon Yu, Suhyun Kim, Soo-Mook Moon
- Test-Time Robust Personalization for Federated Learning
Liangze Jiang, Tao Lin
- FedDAR: Federated Domain-Aware Representation Learning
Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li
- Machine Unlearning of Federated Clusters
Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, Olgica Milenkovic
- Effective passive membership inference attacks in federated learning against overparameterized models
Jiacheng Li, Ninghui Li, Bruno Ribeiro
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- Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
Liam H Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein
- Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated
John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael Rabbat
- Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, Reza Shokri
- Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation
Hong-Min Chu, Jonas Geiping, Liam H Fowl, Micah Goldblum, Tom Goldstein
- EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
Michael Crawshaw, Yajie Bao, Mingrui Liu
- Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
Boxin Zhao, Ziqi Liu, Chaochao Chen, mladen kolar, Zhiqiang Zhang, JUN ZHOU
- Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
- Single-shot General Hyper-parameter Optimization for Federated Learning
Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig
- CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
Samuel Maddock, Alexandre Sablayrolles, Pierre Stock
- Hyperparameter Optimization through Neural Network Partitioning
Bruno Kacper Mlodozeniec, Matthias Reisser, Christos Louizos
- Instance-wise Batch Label Restoration via Gradients in Federated Learning
Kailang Ma, Yu Sun, Jian Cui, Dawei Li, Zhenyu Guan, Jianwei Liu
- Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
Andrew Lowy, Meisam Razaviyayn
本文参考：AI TIME 论道
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