Author : Ashu

The rapid growth of Internet of Things (IoT) devices has generated unprecedented volumes of data, necessitating efficient and secure data processing mechanisms. Federated learning (FL) has emerged as a promising solution to enable collaborative model training while preserving user privacy. This paper explores the design of efficient federated learning models tailored for edge computing in IoT environments. Key challenges such as resource constraints, communication overhead, and data heterogeneity are addressed, and innovative optimization techniques are proposed to enhance performance. The study demonstrates the viability of privacy-preserving FL in real-world IoT scenarios through a combination of theoretical analysis and experimental validation.

Keywords: Federated Learning (FL)m Privacy-Preserving, Edge Computing, IoT Environments