Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning

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 pro...

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Vydáno v:	IEEE transactions on parallel and distributed systems Ročník 33; číslo 3; s. 536 - 550
Hlavní autoři:	Lim, Wei Yang Bryan, Ng, Jer Shyuan, Xiong, Zehui, Jin, Jiangming, Zhang, Yang, Niyato, Dusit, Leung, Cyril, Miao, Chunyan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial intelligence auction Clusters Computational modeling Data models Deep learning Edge computing edge intelligence evolutionary game Federated learning Game theory Games Machine learning Magnetic heads Performance evaluation Resource allocation Resource management Servers Stability analysis Training
ISSN:	1045-9219, 1558-2183
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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.
AbstractList	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.
Author	Xiong, Zehui Zhang, Yang Ng, Jer Shyuan Jin, Jiangming Miao, Chunyan Niyato, Dusit Leung, Cyril Lim, Wei Yang Bryan
Author_xml	– sequence: 1 givenname: Wei Yang Bryan orcidid: 0000-0003-2150-5561 surname: Lim fullname: Lim, Wei Yang Bryan email: limw0201@e.ntu.edu.sg organization: Alibaba Group and Alibaba-NTU Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore, Singapore – sequence: 2 givenname: Jer Shyuan orcidid: 0000-0003-2772-8977 surname: Ng fullname: Ng, Jer Shyuan email: s190068@e.ntu.edu.sg organization: Alibaba Group and Alibaba-NTU Joint Research Institute (JRI), Nanyang Technological University (NTU), Singapore, Singapore – sequence: 3 givenname: Zehui orcidid: 0000-0002-4440-941X surname: Xiong fullname: Xiong, Zehui email: zehui_xiong@sutd.edu.sg organization: Information Systems Technology and Design (ISTD) Pillar, Singapore University of Technology and Design, Singapore, Singapore – sequence: 4 givenname: Jiangming surname: Jin fullname: Jin, Jiangming email: jiangming.jin@outlook.com organization: TuSimple, Beijing 100016, China – sequence: 5 givenname: Yang orcidid: 0000-0001-9229-7689 surname: Zhang fullname: Zhang, Yang email: yangzhang@nuaa.edu.cn organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China – sequence: 6 givenname: Dusit orcidid: 0000-0002-7442-7416 surname: Niyato fullname: Niyato, Dusit email: dniyato@ntu.edu.sg organization: School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore, Singapore – sequence: 7 givenname: Cyril orcidid: 0000-0001-9911-2069 surname: Leung fullname: Leung, Cyril email: cleung@ece.ubc.ca organization: Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Singapore, Singapore – sequence: 8 givenname: Chunyan orcidid: 0000-0002-0300-3448 surname: Miao fullname: Miao, Chunyan email: ascymiao@ntu.edu.sg organization: SCSE, Nanyang Technological University (NTU), Singapore, Singapore
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Snippet	To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge...
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SubjectTerms	Artificial intelligence auction Clusters Computational modeling Data models Deep learning Edge computing edge intelligence evolutionary game Federated learning Game theory Games Machine learning Magnetic heads Performance evaluation Resource allocation Resource management Servers Stability analysis Training
Title	Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning
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