FGFL: A blockchain-based fair incentive governor for Federated Learning

【Author】 Gao, Liang; Li, Li; Chen, Yingwen; Xu, ChengZhong; Xu, Ming

【Source】JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

【Abstract】Federated Learning is a framework that coordinates a large amount of workers to train a shared model in a distributed manner, in which the training data are located on the workers' sides in order to preserve data privacy. There are two challenges in the crowdsourcing of FL, the workers who participant in training need to consume computing and communication resources, so that they are reluctant to participate in the training process if they can not get reasonable rewards. Moreover, there may be attackers who send arbitrary updates to get undeserving compensation or even destroy the model, thus, effective prevention of malicious workers is also critical. An incentive mechanism is urgently required in order to encourage high-quality workers to participate in FL and to punish the attackers. In this paper, we propose FGFL, a blockchain-based incentive governor for Federated Learning. In FGFL, we assess the participants with reputation and contribution indicators. Then the task publisher rewards workers fairly to attract efficient ones while the malicious ones are punished and eliminated. In addition, we propose a blockchain-based incentive management system to manage the incentive mechanism. We evaluate the effectiveness and fairness of FGFL through theoretical analysis and comprehensive experiments. The evaluation results show that FGFL fairly rewards workers according to their corresponding behavior and quality. FGFL increases the system revenue by 0.2% to 3.4% in reliable federations compared with baselines. And in the unreliable scenario where contains attackers, the system revenue of FGFL outperforms the baselines by more than 46.7%.(c) 2022 Elsevier Inc. All rights reserved.

【Keywords】Federated Learning; Incentive mechanism; Attack detection

【标题】FGFL：基于区块链的联邦学习公平激励调控器

【摘要】联邦学习是一种框架，它以分布式方式协调大量工作人员训练共享模型，其中训练数据位于工作人员一侧，以保护数据隐私。 FL的众包存在两个挑战，参与培训的工人需要消耗计算和通信资源，如果无法获得合理的奖励，他们就不愿意参与培训过程。此外，可能会有攻击者发送任意更新以获取不应有的补偿甚至破坏模型，因此有效防止恶意工作者也至关重要。迫切需要一种激励机制，以鼓励优质工人参与 FL 并惩罚攻击者。在本文中，我们提出了 FGFL，一种基于区块链的联邦学习激励调控器。在 FGFL 中，我们使用声誉和贡献指标评估参与者。然后任务发布者公平地奖励工人以吸引高效的工人，而恶意的工人则受到惩罚和淘汰。此外，我们提出了一种基于区块链的激励管理系统来管理激励机制。我们通过理论分析和综合实验评估FGFL的有效性和公平性。评估结果表明，FGFL根据员工相应的行为和素质公平地奖励他们。与基线相比，FGFL 在可靠联邦中的系统收入增加了 0.2% 至 3.4%。并且在包含攻击者的不可靠场景中，FGFL 的系统收入比基线高出 46.7% 以上。(c) 2022 Elsevier Inc. 保留所有权利。

【关键词】联邦学习；激励机制；攻击检测

【发表时间】2022

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】4.542

【翻译者】石东瑛