Privacy-Preserving Federated Learning for IoT Applications

Abstract

Federated learning has emerged as a promising paradigm for training machine learning models on decentralized data sources while preserving data privacy. In the context of the Internet of Things (IoT), where vast amounts of sensitive data are generated and processed at the edge, privacy-preserving federated learning is of paramount importance. This paper presents an in-depth exploration of privacy-preserving federated learning techniques tailored for IoT applications.

We begin by discussing the unique challenges posed by IoT environments, such as limited computational resources, intermittent connectivity, and the need to protect sensitive data from unauthorized access. We then provide a comprehensive overview of the state-of-the-art techniques in federated learning that address these challenges, including secure aggregation, differential privacy, and homomorphic encryption.

Furthermore, we delve into practical use cases where privacy-preserving federated learning can make a significant impact, such as predictive maintenance in industrial IoT, healthcare data analysis, and smart city applications. We present case studies and real-world examples to illustrate the effectiveness and feasibility of these techniques in diverse IoT scenarios.