针对联邦学习中客户端差异大、适应性差的问题，提出新型自适应优化框架。
Client-Centric Federated Adaptive Optimization
- 以客户端为中心设计自适应优化，支持异步聚合与任意参与
- 理论证明收敛速度达到现有最优，实测性能显著优于基线
- 适合真实场景中异构设备并行训练，尤其适用于移动边缘计算
联邦学习（FL）是一种分布式学习范式，客户端在保护数据隐私的前提下协同训练模型。随着客户端和模型规模的扩大，FL面临两大挑战：由高度统计/系统异构性导致的客户端漂移，以及缺乏自适应能力。然而，现有大多数研究基于不切实际的假设，忽略系统异构性。本文提出一种新的客户端中心联邦自适应优化方法，该框架支持任意客户端参与、异步服务器聚合及异构本地计算，这些特性在真实联邦系统中普遍存在，却常被忽视。我们为一般非凸目标提供了严格的收敛性分析，证明其收敛速度达到现有最优。大量实验表明，所提方法在多个基准上均显著优于基线。
原文摘要 · Abstract (English)
Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. With an increasing scale of clients and models, FL encounters two key challenges, client drift due to a high degree of statistical/system heterogeneity, and lack of adaptivity. However, most existing FL research is based on unrealistic assumptions that virtually ignore system heterogeneity. In this paper, we propose Client-Centric Federated Adaptive Optimization, which is a class of novel federated adaptive optimization approaches. We enable several features in this framework such as arbitrary client participation, asynchronous server aggregation, and heterogeneous local computing, which are ubiquitous in real-world FL systems but are missed in most existing works. We provide a rigorous convergence analysis of our proposed framework for general nonconvex objectives, which is shown to converge with the best-known rate. Extensive experiments show that our approaches consistently outperform the baseline by a large margin across benchmarks.
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