FedBPM:A decentralized federated meta-method for heterogeneous and complex image classification via multi-scale feature fusion

Federated learning is a promising privacy-preserving paradigm for collaboratively training global machine learning models across distributed devices. However, traditional federated learning encounters difficulties in training global model and issues of single-point failure when faced with complex and diverse distributed data. In this paper, we propose a decentralized federated learning approach based on meta-learning and equalized multi-scale feature fusion to enhance the accuracy of the classification task. Firstly, we present a platform architecture of blockchain-based federated learning systems for data sharing among clients, which achieves a trusted decentralized framework. In this architecture, we construct personalized federated learning models tailored to individual users by utilizing meta-learning techniques. Secondly, to address the challenge of fusing complex data features from local users, we formulate a novel equalized multi-scale feature fusion method that employs the dual attention mechanism to extract local data features in a fine-grained manner. Experimental results demonstrate that the proposed approach improves the accuracy by approximately 2% and reduces the loss by approximately 0.25 compared to mainstream federated learning methods.