Tri-branch feature pyramid network based on federated particle swarm optimization for polyp segmentation.

Deep learning technology has shown considerable potential in various domains. However, due to privacy issues associated with medical data, legal and ethical constraints often result in smaller datasets. The limitations of smaller datasets hinder the applicability of deep learning technology in the field of medical image processing. To address this challenge, we proposed the Federated Particle Swarm Optimization algorithm, which is designed to increase the efficiency of decentralized data utilization in federated learning and to protect privacy in model training. To stabilize the federated learning process, we introduced Tri-branch feature pyramid network (TFPNet), a multi-branch structure model. TFPNet mitigates instability during the aggregation model deployment and ensures fast convergence through its multi-branch structure. We conducted experiments on four different public datasets：CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB. The experimental results show that the Federated Particle Swarm Optimization algorithm outperforms single dataset training and the Federated Averaging algorithm when using independent scattered data, and TFPNet converges faster and achieves superior segmentation accuracy compared to other models.