Advances, Challenges & Recent Developments in Federated Learning

This has led to the rise of a paradigm shift in machine learning called federated learning (FL) that allows for decentralized model training over distributed data sources. With FL, devices, servers, or edges train the model together without sharing their privacy-sensitive data, effectively addressing the arising data privacy regulation, data residency, and data silos types of issues, among many others. The FL ecosystem has also been through a series of significant developments, leading to the emergence of secure aggregation protocols and federated optimization techniques for better model convergence and performance, though there are still critical roadblocks such as data heterogeneity, communication overhead, and vulnerability to attacks. This paper aims to summarize the current progress, practical limitations, and future research directions on the application of FL, particularly in the healthcare, finance, and Internet of Things domains, as a means of preserving privacy and enhancing learning. The future entails the incorporation of edge computing, decentralized learning frameworks, and privacy-preserving techniques into the picture that has the potential to reshape today’s state-of-the-art FL.