Advancing Security and Efficiency in Federated Learning Service Aggregation for Wireless Networks

Federated Learning (FL) is a distributed machine learning technique where multiple devices can collaboratively train a model without sharing their data. As a result, FL ensures distinct privacy benefits compared to centralized training approaches. However, despite its benefits, FL remains susceptible to reverse-engineering attacks that can uncover sensitive information about the training data from the local updates sent by each participant. To address this issue, we propose a framework for securely and efficiently aggregating the results of FL on multiple devices. We compare and evaluate the performance of two techniques, Homomorphic Encryption (HE) and Secure Multiparty Computation (SMPC), to determine the best method that allows devices to share their learning results without revealing their raw local data. Ultimately, we propose using SMPC protocol as the most effective solution to secure FL. Our framework is experimentally evaluated, and its effectiveness in terms of security, efficiency, and accuracy is demonstrated.