Secure and Efficient Model Aggregation for Resilient Federated Learning

Summary

FedSEA is a federated learning framework that protects clients' local models while enabling accurate aggregation of the protected coefficients. The server applies truncated SVD to the initial global model and distributes quantized bases to clients, which upload only quantized pseudo-singular values. Pairwise symmetric masking, built on Diffie–Hellman key exchange and a pseudo-random generator, is removed during aggregation, and the source reports improved communication efficiency with preserved utility and security.