Dynamic Network-Assisted D2D-Aided Coded Distributed Learning

Today, numerous machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of wireless networks. Distributed real-time ML solutions are highly susceptible to the so-called straggler effect caused by resource heterogeneity, which can be mitigated by v...

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Veröffentlicht in:	IEEE transactions on communications 2023-06, Vol.71 (6), p.1-1
Hauptverfasser:	Zeulin, Nikita, Galinina, Olga, Himayat, Nageen, Andreev, Sergey, Heath, Robert W.
Format:	Artikel
Sprache:	eng
Schlagworte:	coded computing Collaboration Communication Computation offloading Computational modeling Computer aided instruction data compression Data exchange Data models Data processing Device-to-device communication device-to-device communications Distance learning Graph theory Heterogeneity load balancing Machine learning online distributed learning Optimization Real time System dynamics Training Wireless networks
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