Privacy-Enhanced Federated Feature Alignment Method Based on Secure Multi-Party Computation

Federated learning enables collaborative modeling across institutions while preserving data privacy. However, achieving accurate and efficient feature alignment remains a significant challenge, particularly in scenarios with nonoverlapping feature distributions. To address this issue, we propose P-FedAlign, a privacy-enhanced federated feature alignment method based on secure multi-party computation (SMPC). Our approach leverages cryptographic protocols to perform feature matching among participants without exposing raw data, effectively mitigating potential privacy leakage risks. Furthermore, an efficient acceleration mechanism is integrated to reduce protocol overhead and enhance alignment efficiency. Experimental results demonstrate that P-FedAlign outperforms existing methods in terms of alignment accuracy, privacy protection, and computational performance, making it suitable for various federated learning applications.