{"title":"Incentive Mechanism Design for Cross-Device Federated Learning: A Reinforcement Auction Approach","authors":"Gang Li;Jun Cai;Jianfeng Lu;Hongming Chen","doi":"10.1109/TMC.2024.3508260","DOIUrl":null,"url":null,"abstract":"In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3059-3075"},"PeriodicalIF":7.7000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10770572/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.
期刊介绍:
IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.