Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribu...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE eTransactions on network and service management Ročník 20; číslo 2; s. 1502 - 1516
Hlavní autoři:	Albaseer, Abdullatif Mohammed, Abdallah, Mohamed, Al-Fuqaha, Ala, Seid, Abegaz Mohammed, Erbad, Aiman, Dobre, Octavia A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Bandwidth CFL Clients Clustering Convergence Data models Distributed learning Heterogeneity incongruent data distribution Learning non-i.i.d participants scheduling Performance evaluation resource allocation Scheduling Servers Telematics Training Wireless communication
ISSN:	1932-4537, 1932-4537
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!