【Author】
Zhang, Qinnan; Ding, Qingyang; Zhu, Jianming; Li, Dandan
【Source】2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE WORKSHOPS (WCNCW)
【Abstract】Federated learning is a distributed machine learning framework that enables distributed model training with local datasets, which can effectively protect the data privacy of workers (i.e., intelligent edge nodes). The majority of federated learning algorithms assume that the workers are trusted and voluntarily participate in the cooperative model training process. However, the situation in practical application is not consistent with this. There are many challenges such as worker selection schemes for participating workers, which hamper the widespread adoption of federated learning. The existing research about worker selection scheme focused on multi-weight subjective logic model to calculate reputation value and adopted contract theory to motivate workers, which may exist subjective judgmental factors and unfair profit distribution. To address above challenges, we calculate the reputation value by model quality parameters to evaluate the reliability of workers. Blockchain is designed to store historical reputation value that realized tamper-resistance and non-repudiation. Numerical results indicate that the worker selection scheme can improve the accuracy of the model and accelerate the model convergence.
【Keywords】blockchain; federated learning; reputation evaluation; consensus algorithm
【标题】区块链通过工人选择授权可靠的联邦学习：一种值得信赖的声誉评估方法
【摘要】联邦学习是一种分布式机器学习框架，可以使用本地数据集进行分布式模型训练，可以有效保护工作者（即智能边缘节点）的数据隐私。大多数联邦学习算法都假设工人是受信任的并自愿参与合作模型训练过程。但实际应用中的情况与此不符。存在许多挑战，例如参与工人的工人选择计划，这阻碍了联邦学习的广泛采用。现有的工人选拔方案研究集中在多权重主观逻辑模型计算声誉值，采用契约理论激励工人，可能存在主观判断因素和利润分配不公。为了应对上述挑战，我们通过模型质量参数计算声誉值来评估工人的可靠性。区块链旨在存储实现防篡改和不可否认的历史声誉值。数值结果表明，工人选择方案可以提高模型的准确性，加速模型收敛。
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