解决联邦学习中用户退订和设备慢导致的数据缺失问题。
FLOSS: Federated Learning with Opt-Out and Straggler Support
- 引入新机制处理用户主动退订与设备延迟带来的数据缺失
- 模拟实验显示模型性能下降显著减少
- 适合关注隐私合规与系统鲁棒性的研究者
现有联邦学习中的数据隐私研究主要针对同意共享数据的用户。然而，现代数据隐私协议也允许用户在使用系统时自主选择不共享数据。当与异构设备能力导致的计算延迟（即慢节点）结合时，会引发多源数据缺失，造成偏差并降低模型性能。本文提出FLOSS系统，有效缓解因慢节点和用户退订导致的数据缺失对联邦学习的影响，并通过模拟实验验证其有效性。
原文摘要 · Abstract (English)
Previous work on data privacy in federated learning systems focuses on privacy-preserving operations for data from users who have agreed to share their data for training. However, modern data privacy agreements also empower users to use the system while opting out of sharing their data as desired. When combined with stragglers that arise from heterogeneous device capabilities, the result is missing data from a variety of sources that introduces bias and degrades model performance. In this paper, we present FLOSS, a system that mitigates the impacts of such missing data on federated learning in the presence of stragglers and user opt-out, and empirically demonstrate its performance in simulations.
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