FedACT: Federated Agnostic Learning on Limited Decentralized CT Images With Knowledge Transferring Process

Existing federated learning primarily focuses on problem setups where servers and clients engage in model training for one or multiple specific tasks. However, in real-world scenarios, the required diagnoses at clinical sites can vary due to the diversity of conditions, differing not only from each other but also from those at the server. In this study, we concentrate on the practical yet challenging Federated Agnostic Learning (FAL), where client-side diagnostic tasks are agnostic. We introduce a novel FedACT method to address this issue, which is composed of two components. The first component extracts shared features among multiple agnostic tasks using an end-to-end similarity layer based on contrastive learning to enhance generalizability. In the second component, we design personalized task-specific branches for comprehensive tasks, including classification and segmentation. The branches can accurately accomplish the respective tasks through knowledge transfer and enhanced discriminative capabilities across various classes and tasks. Moreover, to better accommodate the potential heterogeneity of data and unseen tasks, specialized updating and aggregation methods are devised for FedACT. The experimental results demonstrate the effectiveness of FedACT in various scenarios under the FAL setting.