论文标题
在联邦学习中，一种用于有效客户选择的多机构强化学习方法
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
论文作者
论文摘要
联合学习（FL）是一种培训技术，它使客户设备能够通过汇总本地计算模型而无需揭示其原始数据来共同学习共享模型。尽管大多数现有工作都集中在提高FL模型的准确性上，但在本文中，我们专注于提高培训效率，这通常是在现实世界中采用FL的障碍。具体而言，我们设计了一个有效的FL框架，该框架共同优化了模型的准确性，处理延迟和沟通效率，所有这些都是实施FL的主要设计考虑因素。受到多代理增强学习（MARL）在解决复杂控制问题方面的成功的启发，我们提出了\ textit {fedmarl}，这是一个基于MARL的FL框架，可执行有效的运行时客户端选择。实验表明，FedMarl可以通过较低的处理延迟和通信成本显着提高模型准确性。
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data. While most of the existing work focuses on improving the FL model accuracy, in this paper, we focus on the improving the training efficiency, which is often a hurdle for adopting FL in real-world applications. Specifically, we design an efficient FL framework which jointly optimizes model accuracy, processing latency and communication efficiency, all of which are primary design considerations for real implementation of FL. Inspired by the recent success of Multi-Agent Reinforcement Learning (MARL) in solving complex control problems, we present \textit{FedMarl}, an MARL-based FL framework which performs efficient run-time client selection. Experiments show that FedMarl can significantly improve model accuracy with much lower processing latency and communication cost.
