To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet. In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private stochastic gradient descent, and identify its limitations. We then propose a unified framework SoteriaFL for private federated learning, which accommodates a general family of local gradient estimators including popular stochastic variance-reduced gradient methods and the state-of-the-art shifted compression scheme. We provide a comprehensive characterization of its performance trade-offs in terms of privacy, utility, and communication complexity, where SoteraFL is shown to achieve better communication complexity without sacrificing privacy nor utility than other private federated learning algorithms without communication compression.
翻译:为了在无线网络等带宽饥饿环境中进行大规模机器学习,最近在设计通信高效联合学习算法方面已经取得重大进展。另一方面,隐私保护,特别是在客户一级,是另一个重要的分流,在先进的通信压缩技术面前尚未同时解决。在本文件中,我们提议了一个统一框架,用通信压缩提高私人联合学习的通信效率。利用一般压缩操作员和当地差异隐私,我们首先研究一种简单算法,将压缩直接用于差异性私人随机梯度下降,并找出其局限性。我们然后提议一个用于私人联动学习的统一框架SoteriaFLL,这个框架将容纳一个本地梯度估计者的一般家庭,包括流行性偏差变变梯度梯度法和州级转换压缩计划。我们全面描述其在隐私、通用和通信复杂性方面的业绩权衡,显示SoteraFLL在不牺牲隐私或功能性能的情况下实现更好的通信复杂性,而不牺牲其他私营联动算法。