The issue of potential privacy leakage during centralized AI's model training
has drawn intensive concern from the public. A Parallel and Distributed
Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new
paradigm to cope with the privacy issue by allowing clients to perform model
training locally, without the necessity to upload their personal sensitive
data. In FL, the number of clients could be sufficiently large, but the
bandwidth available for model distribution and re-upload is quite limited,
making it sensible to only involve part of the volunteers to participate in the
training process. The client selection policy is critical to an FL process in
terms of training efficiency, the final model's quality as well as fairness. In
this paper, we will model the fairness guaranteed client selection as a
Lyapunov optimization problem and then a C2MAB-based method is proposed for
estimation of the model exchange time between each client and the server, based
on which we design a fairness guaranteed algorithm termed RBCS-F for
problem-solving. The regret of RBCS-F is strictly bounded by a finite constant,
justifying its theoretical feasibility. Barring the theoretical results, more
empirical data can be derived from our real training experiments on public
datasets.

本文旨在解决集中式人工智能模型训练时可能存在的隐私泄漏问题。作者提出一种公平保障的客户选择算法，其根据 Lyapunov 优化问题进行建模，通过 C2MAB 估计模型交换时间，称为 RBCS-F。通过理论分析和真实数据实验，证明了该算法的可行性和实用性。