Federated Fuzzy Transfer Learning With Domain and Category Shifts

Abstract

Unsupervised domain adaptation leverages knowledge from source domain(s)/task(s) to facilitate learning in target task, particularly in unsatisfied and complex scenarios with data scarcity and distribution shifts. This approach helps reduce the high costs associated with collecting or labeling data for the target domain. However, it raises privacy concerns due to its matching techniques requiring access to source data, particularly in sensitive applications. In addition, most domain adaptation methods assume that source and target domains share the same label space, disregarding category shifts. In this article, we propose federated fuzzy transfer learning for category shifts (FdFTL) to address the before mentioned challenges-data privacy and category shifts. By combining a hybrid approach of fuzzy model and federated learning, a cloud model capable of performing across domains can be trained without the need for data sharing. This approach also results in a reduction of model parameters compared to traditional methods training individual models from multiple source domains. To eliminate domain and category shifts, we utilize a global clustering and a local semantic consensus clustering to effectively separate known target classes from out-of-distribution samples. Furthermore, we incorporate a confident score and the Silhouette analysis to elaborate the accuracy of categorizing target known classes. Experimental results on real-world visual tasks across universal, open-set, partial, and closed-set scenarios demonstrate the effectiveness of our proposed method.