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Resource Management for MEC Assisted Multi-layer Federated Learning Framework

Li, Huibo, Pan, Yijin, Zhu, Huiling, Gong, Peng, Wang, Jiangzhou (2023) Resource Management for MEC Assisted Multi-layer Federated Learning Framework. IEEE Transactions on Wireless Communications, . ISSN 1536-1276. (In press) (KAR id:103688)


In this paper, a mobile edge computing (MEC) assisted multi-layer architecture is proposed to support the implementation of federated learning in Internet of Things (IoT) networks. In this architecture, when performing a federated learning based task, data samples can be partially offloaded to MEC servers and cloud server rather than only processing the task at the IoT devices. After collecting local model parameters from devices and MEC servers, cloud server makes an aggregation and broadcasts it back to all devices. An optimization problem is presented to minimize the total federated training latency by jointly optimizing decisions on data offloading ratio, computation resource allocation and bandwidth allocation. To solve the formulated NP hard problem, the optimization problem

is converted into quadratically constrained quadratic program (QCQP) and an efficient algorithm is proposed based on semidefinite relaxation (SDR) method. Furthermore, the scenario with the constraint of indivisible tasks in devices is considered and an applicable algorithm is proposed to get effective offloading

decisions. Simulation results show that the proposed solutions can get effective resource allocation strategy and the proposed multi-layer federated learning architecture outperforms the conventional federated learning scheme in terms of the learning latency performance.

Item Type: Article
Uncontrolled keywords: Federated learning, mobile edge computing, cloud radio access network, resource allocation, SDR method
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: University of Kent (
Depositing User: Huiling Zhu
Date Deposited: 06 Nov 2023 07:24 UTC
Last Modified: 08 Nov 2023 10:51 UTC
Resource URI: (The current URI for this page, for reference purposes)

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