A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing
Mobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm. ; This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) through the European Regional Development Fund (ERDF) and by national funds through the FCT, within project POCI-01-0145-FEDER-029074 (ARNET). ; info:eu-repo/semantics/publishedVersion
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing
Mobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm. ; This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) through the European Regional Development Fund (ERDF) and by national funds through the FCT, within project POCI-01-0145-FEDER-029074 (ARNET). ; info:eu-repo/semantics/publishedVersion
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing
Zheng, Jingjing (author) / Li, Kai (author) / Tovar, Eduardo (author) / Guizani, Mohsen (author)
2021-07-02
doi:10.1109/IWCMC51323.2021.9498853
Article (Journal)
Electronic Resource
English
DDC:
690
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