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pro vyhledávání: '"Daniel Scheliga"'
Publikováno v:
Applied Artificial Intelligence, Vol 38, Iss 1 (2024)
Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distribu
Externí odkaz:
https://doaj.org/article/09c8d0aa6936474bb77dbd525abbaac5
