Community Awareness Personalized Federated Learning for Defect Detection

Hongwei Zhao, Qiyuan Liu, Haoyun Sun, Liang Xu, Weishan Zhang, Yikang Zhao, Fei-Yue Wang

Abstract: Multiple organizations in social manufacturing can collaborate on high-quality product defect detection with social networks. Federated learning (FL) is an emerging paradigm where multiple clients can collaboratively train a defect detection model in a privacy-preserving manner. A prevalent issue in FL, concept drift, is discussed in this article. Feature representations of the same label may vary at different clients which affects the performance of FL. To address this issue, a novel community aware personalized federated learning (CA-PFL) is proposed in this article. A graph structured federation social network is constructed with local model updates. Communities in federation network are discovered with community detection to ensure that the same label at different clients have similar representations in each community. Shared layers of local models are aggregated in each community and each local client keeps their personalized layers. Furthermore, a federation community contrastive loss (FedCCL) is proposed to accelerate training convergence by constraining the direction of local model updating. Experimental results on nine datasets demonstrate that CA-PFL achieves higher accuracy and faster convergence than state-of-the-art personalized federated learning methods in concept drifts scenarios.