Bridging Knowledge Discrepancy in Retinal Image Analysis through Federated Multi-Task Learning

Retinal image analysis not only reveals the microscopic structure of the eye but also provides insights into overall health status. Therefore, employing multi-task learning to simultaneously address disease recognition and segmentation in retinal images can improve the accuracy and comprehensiveness of the analysis. Given the need for medical privacy, federated multi-task learning provides an effective solution for retinal image analysis. However, existing federated multi-task learning studies fail to address client resource constraints or knowledge discrepancies between global and local models. To address these challenges, we propose FedBKD, a novel federated multi-task learning framework for retinal image analysis. FedBKD leverages a server-side foundation model and effectively bridges the knowledge discrepancy between the clients and the server. Before local training, the adaptive sub-model extraction module ranks the activation values of neurons in the global model. It extracts the most representative sub-model based on computational resources, thereby facilitating the local adaptation of the global model. Additionally, we design a feature consistency optimization strategy to ensure alignment between the local model and the global foundation model's prior knowledge. This reduces error accumulation in the client sub-model during multi-task learning and ensures better adaptation to local tasks. Experimental results on the multi-center retinal image dataset demonstrate that FedBKD achieves state-of-the-art performance.