By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL), but also preserve privacy of sensitive data uploaded/contributed by participating mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP). However, these advantages come with extra coding cost/complexity and communication overhead (referred to as \emph{privacy cost}) that must be considered given the limited computing/communications resources at MUs/MAP, the rationality and incentive competition among MUs in contributing data to the MAP. This article proposes a novel coded FL-based framework for a privacy-aware mobile application service to address these challenges. In particular, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can propose a contract to the MAP according to its expected trainable local data and privacy-protected coded data. To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging coded FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resource, and asymmetric information between the MAP and MUs. Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to solve it. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare, i.e., total utility of all participating entities, up to 114% under the privacy cost consideration compared with those of baseline methods.
翻译:通过编码计算任务,编码计算不仅能够减轻联合学习(FL)中存在的棘手问题,而且还能够保护参与的移动用户(MUS)向移动应用程序提供者(MAP)拥有的中央服务器上传/贡献的敏感数据的隐私。然而,这些优势伴随着额外的编码成本/复杂度和通信管理费(称为emph{privaity cost)),而鉴于MUs/MAP的计算/通信资源有限,MUs之间在向MAP提供数据时存在理性和激励竞争。本文章提出了一个新的基于代码的基于FL的框架,用于建立对隐私有了解的流动应用的移动应用程序服务,以应对这些挑战。特别是,MAP首先根据Mus提供的信息/内容,为FL进程确定了一套最佳的编码/复杂/复杂和通信管理费管理费(称为emph{privilvact),然后,每个选定的MU可以向MAPA提出合同合同合同,根据预期的当地数据和隐私保护的编码数据。要找到最佳的合同,在MAPA和所有参与的S-al IMU(ial)之间,同时,在不断将一个运行的 Ral-l-MUDUDUL的系统下,同时将一个高成本的系统里程的系统提升一个成本-时间,然后将那些成本-MULLL的系统显示整个的系统的问题。