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Publication search results
found 73 matches
- 2023
- Ahmed Khaled, Chi Jin:
Faster federated optimization under second-order similarity. ICLR 2023 - Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, Xiangyu Zhang:
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. ICLR 2023 - Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, Olgica Milenkovic:
Machine Unlearning of Federated Clusters. ICLR 2023 - Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig:
Single-shot General Hyper-parameter Optimization for Federated Learning. ICLR 2023 - Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit S. Bedi, Furong Huang:
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. ICLR 2023 - Hongyan Chang, Reza Shokri:
Bias Propagation in Federated Learning. ICLR 2023 - Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, Wei-Lun Chao:
On the Importance and Applicability of Pre-Training for Federated Learning. ICLR 2023 - Huancheng Chen, Chianing Wang, Haris Vikalo:
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. ICLR 2023 - Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein:
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. ICLR 2023 - Michael Crawshaw, Yajie Bao, Mingrui Liu:
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. ICLR 2023 - Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet:
Federated Neural Bandits. ICLR 2023 - Yiqun Diao, Qinbin Li, Bingsheng He:
Towards Addressing Label Skews in One-Shot Federated Learning. ICLR 2023 - Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen:
Federated Nearest Neighbor Machine Translation. ICLR 2023 - Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein:
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. ICLR 2023 - Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. ICLR 2023 - Clare Elizabeth Heinbaugh, Emilio Luz-Ricca, Huajie Shao:
Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. ICLR 2023 - Xiaolin Hu, Shaojie Li, Yong Liu:
Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. ICLR 2023 - Berivan Isik, Francesco Pase, Deniz Gündüz, Tsachy Weissman, Michele Zorzi:
Sparse Random Networks for Communication-Efficient Federated Learning. ICLR 2023 - Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi:
FedExP: Speeding Up Federated Averaging via Extrapolation. ICLR 2023 - Liangze Jiang, Tao Lin:
Test-Time Robust Personalization for Federated Learning. ICLR 2023 - Michael Kamp, Jonas Fischer, Jilles Vreeken:
Federated Learning from Small Datasets. ICLR 2023 - Minjae Kim, Sangyoon Yu, Suhyun Kim, Soo-Mook Moon:
DepthFL : Depthwise Federated Learning for Heterogeneous Clients. ICLR 2023 - Jiacheng Li, Ninghui Li, Bruno Ribeiro:
Effective passive membership inference attacks in federated learning against overparameterized models. ICLR 2023 - Ping Liu, Xin Yu, Joey Tianyi Zhou:
Meta Knowledge Condensation for Federated Learning. ICLR 2023 - Andrew Lowy, Meisam Razaviyayn:
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. ICLR 2023 - Kailang Ma, Yu Sun, Jian Cui, Dawei Li, Zhenyu Guan, Jianwei Liu:
Instance-wise Batch Label Restoration via Gradients in Federated Learning. ICLR 2023 - Samuel Maddock, Alexandre Sablayrolles, Pierre Stock:
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. ICLR 2023 - John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael G. Rabbat:
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. ICLR 2023 - Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi:
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. ICLR 2023 - Daiqing Qi, Handong Zhao, Sheng Li:
Better Generative Replay for Continual Federated Learning. ICLR 2023
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