Principles and Components of Federated Learning Architectures
This research is significant for machine learning practitioners and researchers as it provides a comprehensive understanding of federated learning architectures, addressing the limitations and potential applications of this incremental approach.
This research tackles the problem of understanding federated learning architectures, providing an in-depth explanation of its key concepts and features, and proposes avenues for future work. The result is a set of architectural patterns for federated learning systems.
Federated Learning (FL) is a machine learning framework where multiple clients, from mobiles to enterprises, collaboratively construct a model under the orchestration of a central server but still retain the decentralized nature of the training data. This decentralized training of models offers numerous advantages, including cost savings, enhanced privacy, improved security, and compliance with legal requirements. However, for all its apparent advantages, FL is not immune to the limitations of conventional machine learning methodologies. This article provides an elaborate explanation of the inherent concepts and features found within federated learning architecture, addressing five key domains: system heterogeneity, data partitioning, machine learning models, communication protocols, and privacy techniques. This article also highlights the limitations in this domain and proposes avenues for future work. Besides, we provide a set of architectural patterns for federated learning systems, which are derived from the systematic survey of the literature. The main elements of FL, the fundamentals of Federated Learning, and a few architectural specifics will all be better understood with the aid of this research.