Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 5; pp. 3099 - 3111
• Main Authors: Wu, Hongda, Wang, Ping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Algorithms; Computational modeling; Convergence; Data models; Data retrieval; Elections; fast convergence; Federated learning; mobile edge computing; node selection; Nodes; Predictive models; Probabilistic logic; Servers; Training

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectiv...