【Author】
Chen, Xinyan; Wang, Taotao; Zhang, Shengli
【Source】2021 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY (AIBT 2021)
【Abstract】Federated learning (FL) is a new solution to fulfill machine learning (DAL) in a decentralized manner. In FL, a group of participants train their local models using their private data, and then upload the locally trained models to a central server that completes the model aggregation process. Compared with the traditional ML that needs to upload private data of the participants to a server for a centralized training, FL protects the data privacy of participants. However, the existing FL has several drawbacks: FL relies to a large extent on a single central server that takes the risk of single point of failure; it lacks a fair and effective reward system to prevent malicious participants from damage the learning process. To overcome these drawbacks, this paper proposes a new FL system based on blockchain (BC). With BC, we introduce a voting-based reputation mechanism to guarantee that the locally trained models front effective FL participants (other than malicious FL participants) can be selected to improve the efficiency of model aggregation. Besides that, we combine the reputation system with a reward mechanism to dynamically adjust the reward shares that can be fairly allocated to the participants. This can attract more FL participants to participate in the learning process, so that the system can run stably for a long time. The experimental results show that our system can achieve more efficient FL learning and can also defend against malicious attacks to a certain extent.
【Keywords】federated learning; blockchain; reputation system; voting-based mechanism
【摘要】联邦学习 (FL) 是一种以去中心化方式实现机器学习 (DAL) 的新解决方案。在 FL 中，一组参与者使用他们的私有数据训练他们的本地模型，然后将本地训练的模型上传到完成模型聚合过程的中央服务器。与传统的机器学习需要将参与者的隐私数据上传到服务器进行集中训练相比，FL 保护了参与者的数据隐私。但是，现有的 FL 有几个缺点： FL 很大程度上依赖于单个中央服务器，冒着单点故障的风险；它缺乏公平有效的奖励制度来防止恶意参与者破坏学习过程。为了克服这些缺点，本文提出了一种基于区块链（BC）的新型 FL 系统。通过 BC，我们引入了基于投票的信誉机制，以保证可以选择本地训练的模型前端有效的 FL 参与者（恶意 FL 参与者除外），以提高模型聚合的效率。除此之外，我们将信誉系统与奖励机制相结合，动态调整可以公平分配给参与者的奖励份额。这样可以吸引更多的 FL 参与者参与到学习过程中，使系统能够长期稳定运行。实验结果表明，我们的系统可以实现更高效的 FL 学习，也可以在一定程度上抵御恶意攻击。
【关键词】联邦学习；区块链；信誉系统；基于投票的机制
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