提出新型非凸联邦学习算法，降低通信频率并解决客户端漂移问题。
Non-convex composite federated learning with heterogeneous data
- 分离近端算子计算与通信，客户端用本地更新减少通信次数。
- 在非凸和非光滑条件下实现亚线性和线性收敛，误差有界。
- 适合数据异构场景，实测优于现有主流方法。
我们提出一种创新的非凸复合联邦学习算法，将近端算子求解与服务器-客户端通信解耦。每个客户端采用本地更新，每轮通信仅发送一个d维向量，显著降低通信频率，并有效缓解客户端漂移问题。分析中，算法面临的挑战源于解耦策略、本地更新以及问题本身的非凸性和非光滑性。在一般非凸条件下，算法实现亚线性收敛；在满足近端Polyak-Lojasiewicz不等式时，可达到线性收敛至有界残差。数值实验表明，该算法在合成数据和真实数据集上均优于当前最优方法。
原文摘要 · Abstract (English)
We propose an innovative algorithm for non-convex composite federated learning that decouples the proximal operator evaluation and the communication between server and clients. Moreover, each client uses local updates to communicate less frequently with the server, sends only a single d-dimensional vector per communication round, and overcomes issues with client drift. In the analysis, challenges arise from the use of decoupling strategies and local updates in the algorithm, as well as from the non-convex and non-smooth nature of the problem. We establish sublinear and linear convergence to a bounded residual error under general non-convexity and the proximal Polyak-Lojasiewicz inequality, respectively. In the numerical experiments, we demonstrate the superiority of our algorithm over state-of-the-art methods on both synthetic and real datasets.
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