Toward Efficient Homomorphic Encryption-Based Federated Learning: A Magnitude-Sensitivity Approach

Federated Learning (FL) is a privacy-preserving technique that allows clients to collaboratively train models while keeping their local data private from both other clients and the central server. However, the presence of a malicious server or attacker can lead to model inversion attacks, which compromise clients’ private information. To counteract these threats, homomorphic encryption (HE) has been proposed as a solution, enabling encrypted model aggregation by the server without exposing raw data. This ensures that a malicious server or attacker cannot access model parameters or reveal clients’ local information. However, HE introduces substantial computational and communication overhead, making it impractical for many real-world implementations. To address this, recent research suggests selectively encrypting only a subset of most important model parameters while leaving the rest unencrypted. While it is promising, the challenge lies in identifying these key parameters to maintain security while minimizing the additional costs associated with homomorphic operations. In addition, even with a subset of parameters, none of the existing methods offer truly efficient and practical HE-based solution. In general, these approaches fall short when applied to large, complex deep learning models and are impractical beyond basic implementations. This paper introduces a straightforward yet effective parameter selection and encryption strategy for HE-based federated learning. Our goal is to minimize this computational, and communication overhead caused by homomorphic operations while maintaining robust security, ensuring practical applicability in real-world scenarios. We begin by evaluating different implementations of CKKS (Cheon-Kim-Kim-Song)-based HE algorithm (which allows floating point operations on encrypted domain) across various model architectures, systematically analyzing the additional burden HE imposes in terms of processing time and size of the model parameters. Next, we introduce a magnitude-based selective encryption strategy that not only provide same security guarantees compared to existing methods but is also practical for scaling to larger, more complex models in FL environments. Hence, this approach enables developers to make informed decisions that strike an optimal balance between performance and privacy/security, making it feasible for real-world deployments.