Secure Personalized Federated Learning Based on Oblivious Transfer

Federated learning(FL) is a machine learning paradigm designed to protect data privacy and security among multiple clients. It is widely used in industries such as healthcare, finance, and insurance. However, common personalization techniques such as clustering, data augmentation, and knowledge distillation often increase privacy risks or incur high computational costs with methods like homomorphic encryption. To address these challenges, this paper proposes the Secure Personalized Federated Learning (SPFL) algorithm, combining Clustered Federated Learning (CFL) and Oblivious Transfer (OT). Using the Affinity Propagation (AP) clustering algorithm, SPFL groups clients by model similarity to create personalized global models without additional information sharing. OT has achieved efficient delivery of security models. Experimental results show SPFL enhances security, adapts to Non-Independent and Identically Distributed (Non-IID) data, improves global model accuracy by more than 6.5%, and reduces the overall running time under three alpha values by more than 19.82% compared to Paillier encryption.