A survey on federated learning: evolution, applications and challenges

Abstract

Federated learning, a machine learning technique that enables collaborative model training on decentralized data, has gained significant attention in recent years due to its potential to address privacy concerns. This paper explores the evolution, applications, and challenges of federated learning. The research topic focuses on providing a comprehensive understanding of federated learning, its advantages, and limitations. The purpose of the study is to highlight the importance of federated learning in preserving data privacy and enabling collaborative model training. The study conducted a literature review by systematically analyzing relevant papers from peer-reviewed journals, conference proceedings, and reputable sources. The results reveal that federated learning offers a promising solution for collaborative machine learning while addressing concerns related to data privacy and security. The study emphasizes the need for further research in optimizing communication protocols, scalability, and privacy-preserving techniques. Overall, this paper contributes to the understanding of federated learning and its potential for secure and efficient decentralized learning paradigms.