Federated Learning with Homomorphic Encryption: A Privacy-Preserving Solution for Smart Cities
Ali Alqazzaz
Abstract
The rapid proliferation of smart cities has led to an unprecedented generation of sensitive data from interconnected infrastructures, such as healthcare, transportation, energy, and surveillance systems. Ensuring data privacy and security while enabling real-time data analytics remains a critical challenge as traditional centralized processing methods are vulnerable to cyber threats and regulatory constraints. While Federated Learning (FL) offers a decentralized alternative by keeping data local, it remains susceptible to privacy breaches during model updates. Homomorphic Encryption (HE) has emerged as a promising solution for securing computations on encrypted data, but its high computational overhead hinders its applicability in real-time scenarios. To address these limitations, this paper proposes Scalable Privacy-Preserving Federated Learning with Efficient Homomorphic Encryption (SPP-FLHE)—a novel framework that integrates FL with optimized HE techniques and Differential Privacy (DP). The proposed framework reduces computational and communication overhead while ensuring robust privacy protection. Our key contributions include: (1) an enhanced HE scheme that minimizes encryption-related latency, making FL more feasible for large-scale deployments; (2) a dynamic DP noise addition mechanism that strengthens privacy without significantly degrading model accuracy; and (3) the introduction of model compression and gradient sparsification techniques to reduce communication overhead, improving scalability for smart city applications. Extensive experiments on real-world smart city datasets demonstrate that SPP-FLHE achieves 92.6% accuracy, while reducing data transmission by 40% and latency by 43% compared to conventional FL-HE methods. These findings highlight the framework’s practicality for real-time smart city applications, enabling secure and efficient data analytics without compromising privacy. The proposed model represents a significant advancement in privacy-preserving machine learning, offering a scalable and computationally efficient approach for securing smart city infrastructures.