Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks. This implies that the exchange of raw monitoring data should be minimized across the network by bringing the analysis functions closer...

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Bibliographic Details
Main Authors: Brahim Aamer, Hatim Chergui, Mustapha Benjillali, Christos Verikoukis
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-10-01
Series:Frontiers in Communications and Networks
Subjects:
dataset entropy
fast federated learning
non-iid
spectral clustering
stochastic policy
6G
Online Access:https://www.frontiersin.org/articles/10.3389/frcmn.2021.739414/full

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