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Publication search results
found 149 matches
- 2023
- Zhiyuan Zhang, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun:
Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense. NeurIPS 2023 - Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma:
Federated Multi-Objective Learning. NeurIPS 2023 - Xuming An, Li Shen, Han Hu, Yong Luo:
Federated Learning with Manifold Regularization and Normalized Update Reaggregation. NeurIPS 2023 - Sara Babakniya, Zalan Fabian, Chaoyang He, Mahdi Soltanolkotabi, Salman Avestimehr:
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks. NeurIPS 2023 - Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He:
Adaptive Test-Time Personalization for Federated Learning. NeurIPS 2023 - Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang:
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. NeurIPS 2023 - Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett:
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. NeurIPS 2023 - Jun Chen, Hong Chen, Bin Gu, Hao Deng:
Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. NeurIPS 2023 - Yi-Chung Chen, Hsi-Wen Chen, Shun-Gui Wang, Ming-Syan Chen:
SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning. NeurIPS 2023 - Zihan Chen, Howard H. Yang, Tony Q. S. Quek, Kai Fong Ernest Chong:
Spectral Co-Distillation for Personalized Federated Learning. NeurIPS 2023 - Michael Crawshaw, Yajie Bao, Mingrui Liu:
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. NeurIPS 2023 - Li Fan, Ruida Zhou, Chao Tian, Cong Shen:
Federated Linear Bandits with Finite Adversarial Actions. NeurIPS 2023 - Ziqing Fan, Ruipeng Zhang, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang:
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. NeurIPS 2023 - Larry Han, Zhu Shen, José R. Zubizarreta:
Multiply Robust Federated Estimation of Targeted Average Treatment Effects. NeurIPS 2023 - Tiansheng Huang, Sihao Hu, Ka Ho Chow, Fatih Ilhan, Selim F. Tekin, Ling Liu:
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training. NeurIPS 2023 - Insu Jeon, Minui Hong, Junhyeog Yun, Gunhee Kim:
Federated Learning via Meta-Variational Dropout. NeurIPS 2023 - Jinyuan Jia, Zhuowen Yuan, Dinuka Sahabandu, Luyao Niu, Arezoo Rajabi, Bhaskar Ramasubramanian, Bo Li, Radha Poovendran:
FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning. NeurIPS 2023 - Mohammad Mahdi Kamani, Yuhang Yao, Hanjia Lyu, Zhongwei Cheng, Lin Chen, Liangju Li, Carlee Joe-Wong, Jiebo Luo:
Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. NeurIPS 2023 - Taehyeon Kim, Eric Lin, Junu Lee, Christian Lau, Vaikkunth Mugunthan:
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection. NeurIPS 2023 - Guangchen Lan, Han Wang, James Anderson, Christopher G. Brinton, Vaneet Aggarwal:
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates. NeurIPS 2023 - Royson Lee, Minyoung Kim, Da Li, Xinchi Qiu, Timothy M. Hospedales, Ferenc Huszar, Nicholas D. Lane:
FedL2P: Federated Learning to Personalize. NeurIPS 2023 - Gwen Legate, Nicolas Bernier, Lucas Page-Caccia, Edouard Oyallon, Eugene Belilovsky:
Guiding The Last Layer in Federated Learning with Pre-Trained Models. NeurIPS 2023 - Junyi Li, Heng Huang:
Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning. NeurIPS 2023 - Junyi Li, Feihu Huang, Heng Huang:
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. NeurIPS 2023 - Yipeng Li, Xinchen Lyu:
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data. NeurIPS 2023 - Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang:
FedNAR: Federated Optimization with Normalized Annealing Regularization. NeurIPS 2023 - Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava:
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. NeurIPS 2023 - Kangyang Luo, Shuai Wang, Yexuan Fu, Xiang Li, Yunshi Lan, Ming Gao:
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning. NeurIPS 2023 - Jie Ma, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang:
Structured Federated Learning through Clustered Additive Modeling. NeurIPS 2023 - Aniket Murhekar, Zhuowen Yuan, Bhaskar Ray Chaudhury, Bo Li, Ruta Mehta:
Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization. NeurIPS 2023
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