Privacy-Preserving in Federated Learning: A Comparison between Differential Privacy and Homomorphic Encryption across Different Scenarios
Alessio Catalfamo, Maria Carolina Fazio, Antonio Celesti, Massimo Villari · 2025
Federated Learning is a decentralized machine learning paradigm where multiple devices collaboratively train a model while keeping their training data local so to enhance data privacy and reduce communication costs. However, it is vulnerable to some privacy threats, such as model inversion and membership inference attacks, which can expose sensitive data in the devices. This paper explores the integration of different Privacy-Preserving approaches in FL, which are Differential Privacy and Homomorphic Encryption. We assess the impact of these techniques on model accuracy, training efficiency, and computational overhead through experimental evaluations across three different application use cases. Our evaluation results depict the trade-off between privacy enhancement and performance for different scenarios and models. They highlight the effectiveness of combining Federated Learning with advanced cryptographic and privacy-preserving techniques to achieve secure, scalable, and privacy-aware distributed learning.