Efficient Federated Meta-Learning Over Multi-Access Wireless Networks

• Published in: IEEE journal on selected areas in communications Vol. 40; no. 5; pp. 1556 - 1570
• Main Authors: Yue, Sheng, Ren, Ju, Xin, Jiang, Zhang, Deyu, Zhang, Yaoxue, Zhuang, Weihua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Collaborative work; Convergence; device selection; efficiency; Energy consumption; Energy costs; Federated meta-learning; Heterogeneity; Internet of Things; Learning; Loss reduction; multi-access systems; Radio spectra; Resource allocation; Resource management; Servers; Stochastic processes; Training; Wireless networks

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence and corresponding low communication efficiency. In addition, since the av...