Agent Selection Framework for Federated Learning in Resource-Constrained Wireless Networks

Federated learning is an effective method to train a machine learning model without requiring to aggregate the potentially sensitive data of agents in a central server. However, the limited communication bandwidth, the hardware of the agents and a potential application-specific latency requirement i...

Celý popis

Podrobná bibliografie

Vydáno v:	IEEE transactions on machine learning in communications and networking Ročník 2; s. 1265 - 1282
Hlavní autoři:	Raftopoulou, Maria, da Silva, Jose Mairton B., Litjens, Remco, Vincent Poor, H., van Mieghem, Piet
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 2024
Témata:	Accuracy Agent selection Computer Science with specialization in Computer Communication Data models Datavetenskap med inriktning mot datorkommunikation Device selection federated learning machine learning Measurement Servers Training Vectors Wireless networks
ISSN:	2831-316X, 2831-316X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!