Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources

With the recent improvements in mobile and edge computing and rising concerns of data privacy, Federated Learning (FL) has rapidly gained popularity as a privacy-preserving, distributed machine learning methodology. Several FL frameworks have been built for testing novel FL strategies. However, most...

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Vydané v:	Journal of parallel and distributed computing Ročník 203; s. 105103
Hlavní autori:	Banerjee, Roopkatha, Modi, Prince, Vyas, Jinal, Sri Abhijit, Chunduru, Chandrashekar, Tejus, Marisetty, Harsha Varun, Gupta, Manik, Simmhan, Yogesh
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.09.2025
Predmet:	Distributed programming Edge intelligence Federated learning Federated learning Distributed programming Edge intelligence
ISSN:	0743-7315
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