Attention-aided federated learning for dependency-aware collaborative task allocation in edge-assisted smart grid scenarios

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.