Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ozkara, Kaan"'
Statistical heterogeneity of clients' local data is an important characteristic in federated learning, motivating personalized algorithms tailored to the local data statistics. Though there has been a plethora of algorithms proposed for personalized
Externí odkaz:
http://arxiv.org/abs/2402.12537
Autor:
Ozkara, Kaan, Karakus, Can, Raman, Parameswaran, Hong, Mingyi, Sabach, Shoham, Kveton, Branislav, Cevher, Volkan
Following the introduction of Adam, several novel adaptive optimizers for deep learning have been proposed. These optimizers typically excel in some tasks but may not outperform Adam uniformly across all tasks. In this work, we introduce Meta-Adaptiv
Externí odkaz:
http://arxiv.org/abs/2401.08893
A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, thro
Externí odkaz:
http://arxiv.org/abs/2207.01771
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients
Externí odkaz:
http://arxiv.org/abs/2107.13892
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients
Externí odkaz:
http://arxiv.org/abs/2102.11786