Joint Client Selection and CPU Frequency Control in Wireless Federated Learning Networks with Power Constraints

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 8; p. 1183
• Main Authors: Zhou, Zhaohui, Shi, Shijie, Wang, Fasong, Zhang, Yanbin, Li, Yitong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023; MDPI
• Subjects: Algorithms; Analysis; Central processing units; client selection; Clients; Communication; Cost function; CPU frequency control; CPUs; Deceleration; Energy consumption; Equipment and supplies; Federated learning; Frequency control; Heterogeneity; Internet of Things; Iterative methods; Machine learning; Mathematical optimization; Network latency; Optimization; Power consumption; Privacy; Resource allocation; Servers; Teaching; Wireless networks

Federated learning (FL) represents a distributed machine learning approach that eliminates the necessity of transmitting privacy-sensitive local training samples. However, within wireless FL networks, resource heterogeneity introduces straggler clients, thereby decelerating the learning process. Add...