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Title: Dynamic edge association and resource allocation in self-organizing hierarchical federated learning networks
Authors: Lim, Bryan Wei Yang
Ng, Jer Shyuan
Xiong, Zehui
Niyato, Dusit
Miao, Chunyan
Kim, Dong In
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Lim, B. W. Y., Ng, J. S., Xiong, Z., Niyato, D., Miao, C. & Kim, D. I. (2021). Dynamic edge association and resource allocation in self-organizing hierarchical federated learning networks. IEEE Journal On Selected Areas in Communications, 39(12), 3640-3653.
Journal: IEEE Journal on Selected Areas in Communications
Abstract: Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association and resource allocation in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose a hierarchical game framework to study the dynamics of edge association and resource allocation in self-organizing HFL networks. In the lower-level game, the edge association strategies of the workers are modelled using an evolutionary game. In the upper-level game, a Stackelberg differential game is adopted in which the model owner decides an optimal reward scheme given the expected bandwidth allocation control strategy of the edge server. Finally, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.
ISSN: 0733-8716
DOI: 10.1109/JSAC.2021.3118401
Schools: School of Computer Science and Engineering 
Research Centres: Alibaba-NTU Joint Research Institute
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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