MATHEMATICAL MODELING AND COMPUTATIONAL ANALYSIS OF FEDERATED LEARNING CONVERGENCE IN HETEROGENEOUS EDGE INTELLIGENCE SYSTEMS
Keywords:
Federated Learning, Edge Intelligence, Convergence Analysis, Distributed Machine Learning, Edge Computing, Mathematical Modeling, Heterogeneous Systems.Abstract
Federated Learning (FL) has emerged as an important distributed machine learning paradigm for training modelsacross multiple devices while preserving data privacy. The growing adoption of edge intelligence systems, includingInternet of Things (IoT) devices, mobile platforms, and edge servers, has increased interest in deploying federated learning within heterogeneous computing environments
References
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proc. AISTATS, 2017, pp. 1273–1282.
P. Kairouz et al., “Advances and Open Problems in Federated Learning,” Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021.
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Published
2024-07-20
How to Cite
V Ravikumar,N Srivani,Kasapaka Rubenraju. (2024). MATHEMATICAL MODELING AND COMPUTATIONAL ANALYSIS OF FEDERATED LEARNING CONVERGENCE IN HETEROGENEOUS EDGE INTELLIGENCE SYSTEMS . Journal of Computational Analysis and Applications (JoCAAA), 33(07), 3621–3630. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5554
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