Aggregation Strategy with Gradient Projection for Federated Learning in Diagnosis

Federated learning aims to address privacy and data security concerns associated with distributed data resources. However, data across different clients typically is not independently and identically distributed, resulting in different local optimal objectives. This disparity will hinder the convergence and performance of global models. Moreover, the presence of noisy labels in client data further complicates matters, making it harder to efficiently deploying global models on a single client. To overcome these issues, we propose a novel algorithm called Federated Learning Aggregation Strategy with Gradient Projection Memory (FedGPM), which leverages gradient projection to refine the model aggregation process. FedGPM reduces the impact of data heterogeneity by projecting gradients into orthogonal directions to remove inconsistent gradient components. Based on the gradient projection memory, the server maintains a federated projection matrix for each client, accurately quantifying the distribution difference between that client’s data and the rest. Adaptive update strategy is employed for each layer during local model training, based on the consistency of local and others’ gradient directions, ensuring positive contributions to global model progress. Experimental results conducted on disease diagnosis tasks using the OCT dataset, with varying levels of data heterogeneity and noise label ratios, demonstrate the superior performance of our algorithm over state-of-the-art methods.