CFSL: A Credible Federated Self-Learning Framework

Weishan Zhang, Zhicheng Bao, Yuru Liu, Liang Xu, Qinghua Lu, Huansheng Ning, Xiao Wang, Su Yang, Fei-Yue Wang, Zengxiang Li

Abstract: Federated learning can collaboratively train AI models while protecting data privacy. In practical industry environment, non-independent and identically distributed (Non-IID) characteristics of data affect the effectiveness of federated learning. Personalized federated learning can help resolve this, but it cannot adapt to unknown data. In addition, practical applications also call for trusted training environment and remain stable when there are security threats. In this article, we propose a credible federated self-learning (CFSL), based on the idea of hypernetwork supported by blockchain to achieve secured, credible, personalized federated self-learning, especially, for unknown data in Non-IID environment. Extensive experiments on three Non-IID data sets demonstrate the capabilities on adaptive resilience for security attacks and on accuracy of recognizing unknown objects, with good performance at the same time. CFSL outperforms the existing personalized federated learning methods, with an increase in average accuracy by 4.11%.