Paper Info
Title
Energy-Aware Device Scheduling for Joint Federated Learning in Edge-assisted Internet of Agriculture Things
Abstract
Edge-assisted Internet of Agriculture Things (Edge-IoAT) connects massive smart devices managed by edge nodes to collect crop data for distributed computing, such as federated learning, to guide agricultural production. In Edge-IoAT, data are cooperatively collected by edge nodes and the server, i.e., vertically partitioned. In addition, sample size and distribution are different for edge nodes, i.e., horizontally partitioned. Existing federated learning frameworks are not applicable for Edge-IoAT because they do not consider both types of data partitioning simultaneously. Moreover, the excessive energy consumption may cause premature interruption of model training, and spectrum scarcity prevents a portion of edge nodes from communicating with the server. Given limited energy and communication resources, training accuracy relies on how to schedule devices. In this paper, we first propose a joint federated learning framework for Edge-IoAT to cope with both vertically and horizontally partitioned data. After that, we formulate an energy-aware device scheduling problem to assign communication resources to the optimal edge node subset for minimizing the global loss function. Then, we develop a greedy algorithm to find the optimal solution. Experiments in a Nebraska farm show that the proposed framework with energy-aware device scheduling achieves a fast convergence rate, low communication cost, and high modeling accuracy under resource constraints.
Year
DOI
Venue
2022
10.1109/WCNC51071.2022.9771547
2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
Keywords
DocType
ISSN
Internet of Agriculture Things, joint federated learning, resource constraints, device scheduling
Conference
1525-3511
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Chongxiu Yu134.58
Shuaiqi Shen200.34
Kuan Zhang378960.23
Hai Zhao400.34
Yeyin Shi500.34