Journal of Cloud Computing and Internet of Things ISSN: 3107-4391 (Online)

Authors:Kavita Sharma, Mohan Kumar

Abstract:Privacy concerns have become increasingly critical in the era of big data and machine learning. Federated learning has emerged as a promising solution for privacy-preserving machine learning, allowing multiple parties to collaboratively train a model while keeping their data decentralized and secure. This paper provides an in-depth overview of federated learning, its applications, advantages, challenges, and potential future directions. We also include illustrative figures and tables to enhance the understanding of the concepts discussed.

Keywords: Federated Learning, Privacy-Preserving Machine Learning, Decentralized Model Training, Collaborative Model Training, Data Privacy Secure Aggregation, Data Heterogeneity, Communication Efficiency, Edge Computing, Differential Privacy