Paper

  • Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

    Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao, Yaliang Li p14457-14483 from Advances in Neural Information Processing Systems 37
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  • Federated Graph Learning for Cross-Domain Recommendation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Graph Learning for Cross-Domain Recommendation

    Ziqi Yang, Zhaopeng Peng, Zihui Wang, Jianzhong Qi, Chaochao Chen, Weike Pan, Chenglu Wen, Cheng Wang, Xiaoliang Fan p64865-64888 from Advances in Neural Information Processing Systems 37
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  • Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

    Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang p19332-19344 from Advances in Neural Information Processing Systems 35
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  • 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

    Bikang Pan, Wei Huang, 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, Philipp Wiesner, Shinichi Nakajima 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

    Insu Jeon, Minui Hong, Junhyeog Yun, Gunhee Kim 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, Ruipeng Zhang, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang 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

    Michael Crawshaw, Yajie Bao, 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, Li Shen, Han Hu, Yong Luo 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, Ruida Zhou, Chao Tian, Cong Shen 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

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