Name
Abstract | ||
|---|---|---|
With the rapid development of wireless communication technology, a large number of data is generated in the network edge. The combination of mobile edge computing (MEC) and federated learning has become a key technology to reduce various cost and protect users' privacy data in mobile networks. High cost may be caused by the nodes processing parameters in Hierarchical Federated Learning (HFL). In this paper, we investigate the problem of node cooperation for cost minimization in HFL. In order to achieve the stability, we design an online algorithm for node cooperation based on Lyapunov optimization theory(ONCA). In ONCA, the cooperation among nodes can be adjusted adaptively according to the dynamics of system state. D2D is adopted for node cooperation. Nodes prefer to select neighbors with high computing and transmission capabilities to cooperate, so that their capabilities can be fully utilized. In different time slots, nodes can cooperate with different peers in order to reduce the cost. Through extensive simulations, it is verified the performance of ONCA. We also observe the average cost is reduced by 13.86% and 18.04% respectively compared with HierFAVG and FedAvg. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/INFOCOMWKSHPS54753.2022.9798016 | IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) |
Keywords | DocType | ISSN |
Hierarchical Federated Learning (HFL), Mobile Edge Computing (MEC), Node Cooperation, Device-to-Device (D2D) | Conference | 2159-4228 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||