计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 46-52.doi: 10.11896/jsjkx.220500272
梁文雅1, 刘波1, 林伟伟2,3, 严远超1
LIANG Wen-ya1, LIU Bo1, LIN Wei-wei2,3, YAN Yuan-chao1
摘要: 联邦学习(Federated Learning,FL)以多方数据参与为驱动,参与方与中央服务器通过不断交换模型参数,而不是直接上传原始数据的方式来实现数据共享和隐私保护。在实际的应用中,FL全局模型的精确性依赖于多个稳定且高质量的客户端参与,但客户端之间数据质量不平衡的问题会导致在训练过程中客户端处于不公平地位甚至直接不参与训练。因此,如何激励客户端积极可靠地参与到FL中,是保证FL被广泛推广和应用的关键。文中主要介绍了在FL中激励机制的必要性,并根据激励机制在FL训练过程中存在的子问题将现有研究分为面向贡献测量、面向客户选择、面向支付分配以及面向多子问题优化的激励机制。对现有的激励方案进行分析和对比,并在此基础上总结激励机制在发展中存在的挑战,探索FL激励机制未来的研究方向。
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