【Author】
Kang, Jiawen; Xiong, Zehui; Niyato, Dusit; Zou, Yuze; Zhang, Yang; Guizani, Mohsen
【Source】IEEE WIRELESS COMMUNICATIONS
【Abstract】Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, for example, the data poisoning attack, or unintentionally, for example, low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.
【Keywords】Mobile handsets; Task analysis; Reliability; Data models; Training; Data privacy; Training data
【摘要】作为一种很有前途的机器学习方法，联邦学习已经出现，它可以利用来自多个节点（例如移动设备）的分布式个性化数据集来提高性能，同时为移动用户提供隐私保护。在联邦学习中，训练数据作为工作人员在移动设备上广泛分布和维护。中央聚合器通过使用移动设备的本地训练数据从移动设备收集本地更新来更新全局模型，从而在每次迭代中训练全局模型。但是，移动设备（即工作人员）可能会上传不可靠的数据，从而导致联邦学习任务中的欺诈行为。工作人员可能有意地执行不可靠的更新，例如数据中毒攻击，或者无意地执行不可靠的更新，例如由于能量限制或高速移动性导致的低质量数据。因此，在联邦学习任务中找到值得信赖和可靠的工作人员变得至关重要。在本文中，声誉的概念被引入作为一个度量。基于该指标，为联邦学习任务提出了一种可靠的工人选择方案。联盟区块链被用作一种去中心化的方法，用于实现对工人的有效声誉管理，而不会遭到否认和篡改。通过数值分析，证明了所提出的方法可以提高移动网络中联邦学习任务的可靠性。
【关键词】手机；任务分析；可靠性;数据模型；训练;数据隐私；训练数据
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