Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156035
Title: Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning
Authors: Lim, Bryan Wei Yang
Ng, Jer Shyuan
Xiong, Zehui
Jin, Jiangming
Zhang, Yang
Niyato, Dusit
Leung, Cyril
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Lim, B. W. Y., Ng, J. S., Xiong, Z., Jin, J., Zhang, Y., Niyato, D., Leung, C. & Miao, C. (2021). Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning. IEEE Transactions On Parallel and Distributed Systems, 33(3), 536-550. https://dx.doi.org/10.1109/TPDS.2021.3096076
Project: AISG2-RP-2020-019
AISGGC-2019-003
M4082187 (4080)
RG16/20
Journal: IEEE Transactions on Parallel and Distributed Systems
Abstract: To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners' participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.
URI: https://hdl.handle.net/10356/156035
ISSN: 1045-9219
DOI: 10.1109/TPDS.2021.3096076
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: https://doi.org/10.1109/TPDS.2021.3096076.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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