[论文解读] Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach

Dan Ben Ami, Kobi Cohen+1 more|arXiv (Cornell University)|Mar 18, 2023

Advanced Bandit Algorithms Research被引用 8

一句话总结

本文提出了一种用于联邦学习的 Bandit 调度算法（BSFL），在减小训练延迟的同时保持泛化能力，并具备理论的后悔度保证及经验验证。

ABSTRACT

Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy considerations, and communication resources. In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. The server updates the global model and broadcasts it back to the clients. The client selection problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). We analyze BSFL theoretically, and show that it achieves a logarithmic regret, defined as the loss of BSFL as compared to a genie that has complete knowledge about the latency means of all clients. Furthermore, simulation results using synthetic and real datasets demonstrate that BSFL is superior to existing methods.

研究动机与目标

• 需要在联邦学习中高效地选择客户端以平衡延迟与泛化的需求；
• 提出一种新颖的 Bandit Scheduling for FL（BSFL）算法；
• 给出理论分析，证明相对于具有完整延迟知识的 oracle 的对数级后悔度；
• 通过仿真演示 BSFL 相较于现有方法在合成数据和真实数据上的优越性。

提出的方法

• 将客户端调度建模为多臂老虎机问题；
• 开发 BSFL 算法，选择并调度客户端以最小化延迟同时保持泛化能力；
• 提供理论分析，证明相对于具有客户端延迟知识的 genie 的对数后悔度；
• 通过对合成数据和真实数据集的仿真评估 BSFL，并与现有方法进行比较。

实验结果

研究问题

• RQ1 如何在不损害泛化的前提下设计联邦学习中的客户端选择以最小化训练延迟？
• RQ2 用于 FL 的基于赌博机( bandit) 的客户端选择策略能建立哪些后悔度保证？
• RQ3 在合成数据和真实数据上，BSFL 相较于现有客户端选择方法的性能如何？
• RQ4 在何种条件下 BSFL 能在实践中实现近最优的调度？

主要发现

• BSFL 对知道所有客户端延迟均值的 oracle 具有对数后悔度。
• BSFL 在合成数据和真实数据集的仿真中展现出优于现有方法的性能。
• 该方法在降低延迟的同时维持模型在新观测上的泛化能力。
• 理论分析支持所提出调度策略的有效性和可靠性。