Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 3; pp. 536 - 550
• Main Authors: Lim, Wei Yang Bryan, Ng, Jer Shyuan, Xiong, Zehui, Jin, Jiangming, Zhang, Yang, Niyato, Dusit, Leung, Cyril, Miao, Chunyan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; auction; Clusters; Computational modeling; Data models; Deep learning; Edge computing; edge intelligence; evolutionary game; Federated learning; Game theory; Games; Machine learning; Magnetic heads; Performance evaluation; Resource allocation; Resource management; Servers; Stability analysis; Training

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is pro...