Incentive Mechanism Design for Cross-Device Federated Learning: A Reinforcement Auction Approach

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Gang Li;Jun Cai;Jianfeng Lu;Hongming Chen
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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.
跨设备联合学习的激励机制设计:一种强化拍卖方法
在跨设备联邦学习(FL)的操作上下文中,资源(如传输功率、信道和计算资源)的有效分配会显著影响整体性能。现有的跨设备FL研究主要集中在提高训练精度的资源分配或激励参与,而忽略了它们的集成设计,以进一步提高跨设备FL的性能。与已有的工作不同,本文将功率分配、信道分配、用户选择和计算频率分配共同集成到激励机制的设计中。每个移动用户都扮演着买家和卖家的双重角色。由于复杂的资源分配、双重角色场景下的真实性保证以及先验信息的不可获得,所考虑的机制设计问题具有挑战性。为了解决这种组合问题,我们提出了一种强化拍卖机制(RAM),它由两层组成。上层采用混合动作强化学习方案来学习用户选择和支付的结果。在下层,每个选定的移动用户优化其资源,以最大化其效用。理论分析证实,我们提出的RAM保证了个体的合理性和真实性。已经进行了大量的仿真来验证所提出的RAM的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
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.
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