KECO: toward efficient task offloading in mobile edge computing with federated knowledge distillation

With the diversification and personalization of mobile user demands, as well as the rapid development of distributed computing technology, leveraging mobile edge computing task offloading to provide users with convenient services has become a research focus. However, existing methods still face issues such as time consumption, high resource consumption, and data silos. Based on this perspective, we propose the federated Knowledge distillation based mobile Edge Computing task Offloading (KECO) method to achieve accurate task offloading and system energy saving. Through federated distillation learning, a teacher-student model is established, and complex and parameter-rich models are used as teachers to assist in the training of the student model. The teacher model transfers the knowledge and information it had learned to the student to enhance the generalization ability of the student. Finally, using the lightweight and flexible nature of the student model, it is deployed in a distributed system to implement the task offloading strategy in mobile edge computing. Experiments show that KECO performs well in large-scale task classification and allocation, and has efficient, reliable, and wide application potential.