保持简单：具有不可靠客户端的联邦学习的容错性评估
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
- National Institute of Water and Atmospheric Research（国家水与大气研究所）
- University of Newcastle（纽卡斯尔大学）
- University of Waikato（怀卡托大学）
- Roblox（罗布乐思）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文评估了不可靠客户端对联邦学习容错性的真实影响，发现简单的FL算法在现实分类问题中表现优异。
中文摘要 AI 辅助
联邦学习（FL）作为一种新兴的人工智能（AI）方法，支持在多个设备上进行去中心化的模型训练，而无需暴露其本地训练数据。FL在学术界和工业界日益受到欢迎。尽管已有研究提出提升FL的容错性，但不可靠设备（如掉线、配置错误、数据质量差）在现实应用中的真实影响尚未得到充分调查。我们精心挑选了两个具有有限客户端数量的代表性现实分类问题，以更好地分析FL容错性。与直觉相反，简单的FL算法在存在不可靠客户端的情况下表现得出奇地好。
英文摘要
Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unreliable devices (e.g., dropping out, misconfiguration, poor data quality) in real-world applications is not fully investigated. We carefully chose two representative, real-world classification problems with a limited numbers of clients to better analyze FL fault tolerance. Contrary to the intuition, simple FL algorithms can perform surprisingly well in the presence of unreliable clients.
