Federated Learning as a Privacy Solution - An Overview

• Published in: Procedia computer science Vol. 217; pp. 316 - 325
• Main Authors: Khan, Mashal, Glavin, Frank G., Nickles, Matthias
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: Decentralized; Distributed Machine Learning; Federated Learning; Privacy Preservation; Security; Distributed Machine Learning; Federated Learning; Decentralized; Security; Privacy Preservation
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2022.12.227

The Fourth Industrial Revolution suggests smart and automated industrial solutions by incorporating Artificial Intelligence into it. Today, the world of technology is highly dependent on Machine Learning (ML) and Deep Learning (DL) and their applications. All these ML/DL models, which bring huge benefits and provide Industry 4.0 solutions, require a bulk of data, extensive computational power, and storage for enhanced performance and accuracy. With the current jurisdictions on privacy all over the world, it is hard to access the required amount of data without giving the data ownership to the centralized silos. Taking model to the data source is the idea that makes Federated Learning (FL) a unique and better-suited solution in this situation. In this paper, we present a review of FL, its learning models, aggregation algorithms, frameworks, and the challenges faced by this new paradigm of decentralized and distributed Machine Learning. We discuss the potential applications of FL in various domains that can help improve the efficiency and flexibility of industrial processes. We also talk about their impact on changing the model training trends altogether in terms of data privacy, decentralization, security, and resource management. The main contribution of this work is to provide a comprehensive and concise review and comparative analysis of various frameworks and aggregation algorithms, followed by a discussion of challenges currently faced by FL.