Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as \emph{Confederated Learning} (CFL), in order to accommodate a larger number of users. A CFL system is composed of multiple networked edge servers, with each server connected to an individual set of users. Decentralized collaboration among servers is leveraged to harness all users' data for model training. Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system. We propose a stochastic gradient method for distributed learning in the CFL framework. The proposed method incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component to effectively reduce communication overhead. Relying on a delicately designed triggering condition, the CTUS mechanism allows each server to select only a small number of users to upload their gradients, without significantly jeopardizing the convergence performance of the algorithm. Our theoretical analysis reveals that the proposed algorithm enjoys a linear convergence rate. Simulation results show that it achieves substantial improvement over state-of-the-art algorithms in terms of communication efficiency.

翻译：联邦学习是一种无需收集分散于多个数据源的本地数据即可实现模型训练的机器学习范式。传统联邦学习采用单一服务器，仅能支持有限数量的用户，导致学习能力下降。本文考虑一种多服务器联邦学习框架——联邦联邦学习，以容纳更多用户。CFL系统由多个联网的边缘服务器组成，每台服务器连接各自独立用户群。通过利用服务器间的去中心化协作，汇聚所有用户数据进行模型训练。由于涉及大量用户，降低CFL系统的通信开销至关重要。我们提出一种适用于CFL框架的分布式学习随机梯度方法。该方法的核心组件是条件触发用户选择机制，可有效降低通信开销。基于精心设计的触发条件，CTUS机制允许每个服务器仅选择少量用户上传梯度，而不会显著影响算法的收敛性能。理论分析表明，所提算法具有线性收敛速度。仿真结果显示，该算法在通信效率方面较现有最优算法有显著提升。