Federated Learning and Privacy-Preserving AI:...

Federated Learning and Privacy-Preserving AI: Challenges and Solutions in Distributed Machine Learning

Author Name : Dheerender Thakur

ABSTRACT Federated Learning (FL) and Privacy-Preserving AI represent significant advancements in machine learning, focusing on decentralizing model training while maintaining data privacy. Federated Learning enables collaborative training of models across multiple devices without sharing raw data, thereby addressing privacy concerns inherent in centralized systems. However, this decentralized approach introduces challenges, including data heterogeneity, communication overhead, and potential privacy risks such as data leakage and model inversion. Privacy-preserving techniques such as differential privacy, secure aggregation, and homomorphic encryption are employed to mitigate these risks. This paper explores the fundamentals of Federated Learning, its privacy challenges, and the effectiveness of various privacy-preserving techniques. It also examines realworld applications and case studies, highlighting the impact and potential of Federated Learning in various domains. The discussion concludes with an overview of future research directions aimed at enhancing privacy, scalability, and robustness in Federated Learning systems.