提出安全误差反馈算法，解决联邦学习通信压缩中的安全与效率难题。
Safe-EF: Error Feedback for Nonsmooth Constrained Optimization
- 设计新算法Safe-EF，结合误差反馈与安全约束，适配非光滑凸优化。
- 理论证明其复杂度逼近最优下界，通信开销显著降低。
- 适用于机器人训练等需安全约束的分布式场景，实验验证有效。
联邦学习因模型更新维度高面临严重通信瓶颈。实践中常采用收缩压缩器（如Top-K）以降低通信开销，但可能损害性能。误差反馈（EF）可缓解此问题，但以往主要针对光滑无约束问题，难以应用于含非光滑目标和安全约束的实际场景。本文在经典非光滑凸设定下，建立了一阶算法结合收缩压缩的新型下界复杂度。进而提出Safe-EF，其复杂度逼近该下界（至多常数因子），同时保证安全约束，适用于实际应用。进一步将方法扩展至随机设置，弥合理论与实践差距。在模拟分布式人形机器人训练的强化学习场景中，大量实验验证了Safe-EF在保障安全性的同时显著降低通信复杂度的有效性。
原文摘要 · Abstract (English)
Federated learning faces severe communication bottlenecks due to the high dimensionality of model updates. Communication compression with contractive compressors (e.g., Top-K) is often preferable in practice but can degrade performance without proper handling. Error feedback (EF) mitigates such issues but has been largely restricted for smooth, unconstrained problems, limiting its real-world applicability where non-smooth objectives and safety constraints are critical. We advance our understanding of EF in the canonical non-smooth convex setting by establishing new lower complexity bounds for first-order algorithms with contractive compression. Next, we propose Safe-EF, a novel algorithm that matches our lower bound (up to a constant) while enforcing safety constraints essential for practical applications. Extending our approach to the stochastic setting, we bridge the gap between theory and practical implementation. Extensive experiments in a reinforcement learning setup, simulating distributed humanoid robot training, validate the effectiveness of Safe-EF in ensuring safety and reducing communication complexity.
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