C. Wang, B. Jia, H. Yu, L. Chen, K. Cheng and X. Wang, "Attention-aided Federated Learning for Dependency-Aware Collaborative Task Allocation in Edge-Assisted Smart Grid Scenarios," 2022 IEEE/CIC International Conference on Communications in China (ICCC), 2022, pp. 856-861, doi: 10.1109/ICCC55456.2022.9880777.
Attention-aided federated learning for dependency-aware collaborative task allocation in edge-assisted smart grid scenarios
|Author:||Wang, Chenyang1; Jia, Bosen2; Yu, Hao3;|
1College of Intelligence and Computing, Tianjin University, Tianjin, China
2Tianjin International Engineering Institute, Tianjin University, Tianjin, China
3Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
4Information & Telecommunication Branch, State Grid HeBei Electric Power Company, Shijiazhuang, China
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301031304
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-03
With the significant improvement of the intelligent capabilities of smart devices accompanied by the increasingly high requirements. Edge computing is regarded as an effective solution to achieve rapid response by deploying applications and tasks close to users. However, many studies only consider complete offloading, or offload tasks to edge servers in any proportion when designing the allocation strategies, ignoring the dependencies between subtasks. To deal with the dynamic environment, some learning-based task allocation methods generally adopt a centralized training way, which leads to the excessive network transmission resource consumption, especially in the smart grid scenario. To tackle the aforementioned challenges, we investigate the collaborative task allocation (CTA) problem by jointly considering the difference between the benefit of the tasks execution under a certain allocation strategy and when all tasks are executed locally. In this paper, the objective is to maximize the system gain, and we propose an attention-aided federated learning algorithm to deal with the CTA problem, named AteFL, by learning a shared model and extracting the system context for better representing the network information. The simulation results also show the superiority of the proposed AteFL algorithm.
|Pages:||856 - 861|
2022 IEEE/CIC International Conference on Communications in China (ICCC)
IEEE/CIC International Conference on Communications in China
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported partially by the Science and Technology Research of State Grid Corporation of China “Research on Data Sharing and Model Fusion Technology of Power Marketing based on Federated Learning”, Grant No. 5700-202113262A-0-0-00, and the Chinese Government Scholarship (NO. 202006250167) awarded by China Scholarship Council.
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