Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

• Published in: IEEE/ACM transactions on networking Vol. 29; no. 1; pp. 398 - 409
• Main Authors: Dinh, Canh T., Tran, Nguyen H., Nguyen, Minh N. H., Hong, Choong Seon, Bao, Wei, Zomaya, Albert Y., Gramoli, Vincent
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational modeling; Convergence; Data models; Distributed machine learning; Energy consumption; Federated learning; Heterogeneity; Machine learning; Optimization; optimization decomposition; Power consumption; Resource allocation; Resource management; Tradeoffs; Training; Wireless communication; Wireless networks

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advantages such as preserving data privacy,...