Federated Learning and Cryptography: A Secure Framework for IoT Data Privacy

Abstract

As the Internet of Things (IoT) continues to expand, the protection of user data privacy and the security of IoT systems have become increasingly critical. This paper proposes a secure framework that integrates Federated Learning (FL) with advanced cryptographic techniques to address privacy concerns while enabling collaborative machine learning across heterogeneous IoT devices. The proposed framework allows local data processing on IoT devices, ensuring that sensitive information remains decentralized and is never exposed to potential breaches during model training. By employing techniques such as Additive Homomorphic Encryption (AHE) and Secure Multi-Party Computation (SMPC), our approach enables the secure aggregation of model updates while maintaining data confidentiality and integrity. Furthermore, the framework effectively minimizes communication overhead and computational demands, making it suitable for resource-constrained IoT environments. This study not only enhances privacy protections and data security but also improves the efficiency of data analytics in smart cities, healthcare, and industrial applications. Future work will investigate the scalability and adaptability of this framework in real-world scenarios, paving the way for a more secure and privacy-oriented IoT ecosystem.