Block-RACS: Towards Reputation-Aware Client Selection and Monetization Mechanism for Federated Learning

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zahra Batool, Kaiwen Zhang, Matthew Toews
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引用次数: 0

Abstract

Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that integrating blockchain and smart contract technologies into FL can solve the two aforementioned issues. In this paper, we present Block-RACS (Blockchain-based Reputation Aware Client Selection), a mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. Block-RACS employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows Block-RACS to measure the relative contribution of each client by calculating a Shapley value and allocating rewards accordingly. Moreover, a blockchain-based reputation mechanism enables audibility and non-repudiation. The security analysis of the system is also presented to check the security vulnerabilities. We have implemented Block-RACS using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that Block-RACS outperforms existing baseline schemes by improving accuracy and reducing the number of FL rounds.
区块- racs:面向联邦学习的声誉感知客户选择和货币化机制
联邦学习(FL)是一种很有前途的解决方案,可以使用从异构来源(例如,移动设备)收集的数据进行训练,同时避免传输大量原始数据并保护隐私。当前的FL方法以迭代的方式操作,通过每轮选择参与者的子集,要求他们在全局模型的最新版本上使用最新的本地数据进行训练,然后收集这些本地模型更新并将它们聚集起来形成全局模型的下一个迭代,等等,直到达到收敛。不幸的是,现有的FL方法通常会随机选择每轮使用的客户端集,这可能会对训练模型的质量产生负面影响,并且由于离散问题而影响了训练周期。此外,应该鼓励客户,特别是资源有限的移动设备参与,因为联邦学习本质上是人工智能的一种众包形式,需要盈利。我们认为,将区块链和智能合约技术集成到FL中可以解决上述两个问题。在本文中,我们提出了Block-RACS(基于区块链的声誉感知客户端选择),这是一种在智能合约中运行的FL机制,该机制奖励客户端使用加密货币的参与。Block-RACS采用一种多维拍卖机制,根据每个客户端提供的计算和网络资源以及本地数据的质量来选择用户。该拍卖通过智能合约以可靠和可审计的方式实现。这允许block - rac通过计算Shapley值并相应地分配奖励来衡量每个客户端的相对贡献。此外,基于区块链的声誉机制实现了可审计性和不可抵赖性。对系统进行了安全分析,检查系统存在的安全漏洞。我们已经使用solididity实现了Block-RACS,并在以太坊区块链上使用各种流行的数据集进行了测试。我们的研究结果表明,Block-RACS通过提高精度和减少FL回合数来优于现有的基线方案。
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
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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