PJPFL: Personalized federated learning with privacy preservation based on sample similarity

Federated learning (FL) is a distributed machine learning paradigm that. However, existing approaches struggle to achieve both privacy protection and effective personalization. Moreover, existing methods they assume users will always adopt personalized updates, overlooking the need for flexible control—allowing users to decide whether to personalize based on their specific requirements. In this paper, we propose PJPFL, a novel personalized federated learning method that enables a flexible trade-off between the global model’s generalization ability and personalized updates derived from local data. By integrating private set intersection (PSI) and Jaccard similarity, PJPFL allows users to customize model updates based on their individual needs while preserving privacy. To further enhance security, we employ homomorphic encryption (HE) to protect model gradients and parameters from inference attacks, a known vulnerability in FL. Experimental results demonstrate that PJPFL significantly improves model adaptability to local data environments, outperforming both FedAvg and FedProx in personalized update scenarios without incurring additional computational or communication overhead.