提出新算法，让联邦学习通信轮次更少且理论可证明。
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
- 用投影优化方法设计多轮本地计算的联邦算法
- 通信轮次仅需1/ε，总梯度调用为1/ε²
- 适合追求低通信开销的非光滑凸优化场景
多轮本地计算是实现通信高效联邦学习的关键。然而，在无数据异构性约束的通用非光滑凸问题中，此类算法的理论保证长期缺失。本文利用投影高效的优化方法，提出 FedMLS 算法，该算法在多轮本地计算下仍具有可证明的性能提升。FedMLS 在 $/mathcal{O}(1/ε)$ 次通信轮次内达到 $ε$-次优解，总共需要 $/mathcal{O}(1/ε^2)$ 次随机次梯度预言机调用。
原文摘要 · Abstract (English)
Multiple local steps are key to communication-efficient federated learning. However, theoretical guarantees for such algorithms, without data heterogeneity-bounding assumptions, have been lacking in general non-smooth convex problems. Leveraging projection-efficient optimization methods, we propose FedMLS, a federated learning algorithm with provable improvements from multiple local steps. FedMLS attains an $ε$-suboptimal solution in $\mathcal{O}(1/ε)$ communication rounds, requiring a total of $\mathcal{O}(1/ε^2)$ stochastic subgradient oracle calls.
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