Work

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

Year: 2025

Type: article

Abstract: 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.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.3021

Authors: Zhuang Liu, Dong Li, Panfei Yang

Institution: China Electronic Product Reliability and Environmental Test Institute

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,395