Federated Learning in Edge Computing Environments: Opportunities, Challenges, and Future Directions
Abstract: Federated Learning (FL) is a decentralized machine learning approach that enables model training across multiple devices while preserving data privacy. When applied to edge computing environments, FL provides a range of benefits, including reduced latency, bandwidth efficiency, and enhanced data privacy. This paper explores the current state of FL in edge computing, examines the unique challenges posed by these environments, and identifies future research directions to further develop this emerging field.
Keywords: Federated Learning, edge computing, data privacy, decentralized machine learning, future research
How to Cite?: Shaveta, "Federated Learning in Edge Computing Environments: Opportunities, Challenges, and Future Directions", Volume 13 Issue 9, September 2024, International Journal of Science and Research (IJSR), Pages: 275-278, https://www.ijsr.net/getabstract.php?paperid=ES24903163414, DOI: https://dx.doi.org/10.21275/ES24903163414