Paper

  • Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method

    Wei Huang, Bikang Pan, Ye Shi p30590-30623 from Advances in Neural Information Processing Systems 37
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  • Federated Learning over Connected Modes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning over Connected Modes

    Dennis Grinwald, Shinichi Nakajima, Philipp Wiesner p85179-85202 from Advances in Neural Information Processing Systems 37
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  • Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis

    Michael Crawshaw, Mingrui Liu p8240-8299 from Advances in Neural Information Processing Systems 37
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  • Federated Learning via Meta-Variational Dropout

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning via Meta-Variational Dropout

    Minui Hong, Insu Jeon, Gunhee Kim, Junhyeog Yun p11168-11193 from Advances in Neural Information Processing Systems 36
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  • Federated Learning with Bilateral Curation for Partially Class-Disjoint Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning with Bilateral Curation for Partially Class-Disjoint Data

    Ziqing Fan, Bo Han, Yanfeng Wang, Jiangchao Yao, Ruipeng Zhang, Ya Zhang p32006-32019 from Advances in Neural Information Processing Systems 36
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  • Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds

    Yajie Bao, Michael Crawshaw, Mingrui Liu p6467-6508 from Advances in Neural Information Processing Systems 36
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  • Federated Learning with Manifold Regularization and Normalized Update Reaggregation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning with Manifold Regularization and Normalized Update Reaggregation

    Xuming An, Han Hu, Yong Luo, Li Shen p55097-55109 from Advances in Neural Information Processing Systems 36
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  • Federated Linear Bandits with Finite Adversarial Actions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Linear Bandits with Finite Adversarial Actions

    Li Fan, Cong Shen, Chao Tian, Ruida Zhou p62549-62560 from Advances in Neural Information Processing Systems 36
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  • Federated Model Heterogeneous Matryoshka Representation Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Model Heterogeneous Matryoshka Representation Learning

    Xiaoxiao Li, Xiaoguang Liu, Chao Ren, Gang Wang, Liping Yi, Han Yu p66431-66454 from Advances in Neural Information Processing Systems 37
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  • Federated Multi-Objective Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Multi-Objective Learning

    Chaosheng Dong, Jia Liu, Zhuqing Liu, Michinari Momma, Haibo Yang p39602-39625 from Advances in Neural Information Processing Systems 36
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  • Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning

    Shicong Cen, Yuxin Chen, Yuejie Chi, Yuting Wei, Tong Yang p121304-121375 from Advances in Neural Information Processing Systems 37
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  • Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups

    Fengyu Gao, Ruiquan Huang, Jing Yang p125547-125587 from Advances in Neural Information Processing Systems 37
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