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AuthorMhaisen N.
AuthorAbdellatif A.A.
AuthorMohamed A.
AuthorErbad A.
AuthorGuizani M.
Available date2022-04-21T08:58:20Z
Publication Date2022
Publication NameIEEE Transactions on Network Science and Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TNSE.2021.3053588
URIhttp://hdl.handle.net/10576/30050
AbstractDistributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models' parameters into a global model. Federated learning is a promising paradigm that allows for extending local training among the participant devices before aggregating the parameters, offering better communication efficiency. However, in the cases where the participants' data are strongly skewed (i.e., non-IID), the local models can overfit local data, leading to low performing global model. In this paper, we first show that a major cause of the performance drop is the weighted distance between the distribution over classes on users' devices and the global distribution. Then, to face this challenge, we leverage the edge computing paradigm to design a hierarchical learning system that performs Federated Gradient Descent on the user-edge layer and Federated Averaging on the edge-cloud layer. In this hierarchical architecture, we formalize and optimize this user-edge assignment problem such that edge-level data distributions turn to be similar (i.e., close to IID), which enhances the Federated Averaging performance. Our experiments on multiple real-world datasets show that the proposed optimized assignment is tractable and leads to faster convergence of models towards a better accuracy value. 2013 IEEE.
Languageen
PublisherIEEE Computer Society
SubjectCombinatorial optimization
SubjectGradient methods
SubjectOptimization
SubjectAssignment problems
SubjectCommunication efficiency
SubjectDistributed optimization
SubjectHierarchical architectures
SubjectHierarchical learning
SubjectLocal distributions
SubjectStatistical properties
SubjectUbiquitous environments
SubjectLearning systems
TitleOptimal User-Edge Assignment in Hierarchical Federated Learning Based on Statistical Properties and Network Topology Constraints
TypeArticle
Pagination55-66
Issue Number1
Volume Number9


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