(r\)Age-\(k\): Communication-Efficient Federated Learning Using Age Factor

Federated learning (FL) is a collaborative approach where multiple clients, coordinated by a parameter server (PS), train a unified machine-learning model. The approach, however, suffers from two key challenges: data heterogeneity and communication overhead. Data heterogeneity refers to inconsistenc...

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Veröffentlicht in:	arXiv.org 2024-10
Hauptverfasser:	Matin Mortaheb, Kaswan, Priyanka, Ulukus, Sennur
Format:	Artikel
Sprache:	eng
Schlagworte:	Age Age factors Algorithms Clients Communication Federated learning Heterogeneity Machine learning Parameter identification
Online-Zugang:	Volltext
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