Federated Learning for Privacy-Preserving Machine Learning

Abstract

Because of the rapid rise of data-driven applications, there are now significant concerns around the privacy of data, the security of data, and the compliance of machine learning systems with regulatory requirements. The traditional methods of centralized learning necessitate the accumulation of sensitive data on a central server, which raises the possibility of data leakage and misuse. Because it enables model training across distant devices without directly sharing raw data, federated learning has emerged as a potential paradigm for privacy-preserving machine learning. This is because it allows for the training of models more efficiently. federated learning is a framework for decentralized learning that enables several clients to train a shared model in a collaborative manner while maintaining the data's localization. The fundamental concepts of federated learning, which include the implementation of safe aggregation, communication-efficient training, and periodic updates to the local model. In order to emphasize the practical significance of this topic, applications in the fields of healthcare, banking, mobile devices, and Internet of Things environments are discussed. Federated learning strikes a good compromise between the effectiveness of models and the privacy of data, making it acceptable for use in domains that are sensitive and regulated. Despite this, there are still difficulties that require attention, such as communication overhead, heterogeneity of data, security concerns, and scalability of the system. In its conclusion, the study highlights potential future research areas that are centered on enhancing the robustness, efficiency, and privacy guarantees of federated learning systems.