Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 11; pp. 2687 - 2700
• Main Authors: Feng, Jie, Liu, Lei, Pei, Qingqi, Li, Keqin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Collaborative work; Computational modeling; Convergence; CPU-cycle frequency; Data models; Decomposition; Federated learning; Heuristic methods; Iterative algorithms; local accuracy; Machine learning; min-max cost; Mixed integer; Nonlinear programming; Optimization; Resource management; Servers; Smart devices; Subcarriers; Training; wireless edge networks; Wireless networks

Federated learning is a distributed machine learning technology that can protect users' data privacy, so it has attracted more and more attention in the industry and academia. Nonetheless, most of the existing works focused on the cost optimization of the entire process, while the cost of indiv...