An Efficient Federated Learning Framework for Training Semantic Communication Systems

Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Exi...

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Vydané v:	IEEE transactions on vehicular technology Ročník 73; číslo 10; s. 15872 - 15877
Hlavní autori:	Nguyen, Loc X., Le, Huy Q., Tun, Ye Lin, Aung, Pyae Sone, Tun, Yan Kyaw, Han, Zhu, Hong, Choong Seon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Communication Communication overhead Communications systems Communications traffic Data models Decoding deep joint source-channel coding (JSCC). federated learning Deep learning Feature extraction Federated learning Privacy Redundancy semantic communication Semantics Training Transformers
ISSN:	0018-9545, 1939-9359
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