Decentralised federated learning with adaptive partial gradient aggregation

Decentralised federated learning with adaptive partial gradient aggregation

Federated learning aims to collaboratively train a machine learning model with possibly geo-distributed workers, which is inherently communication constrained. To achieve communication efficiency, the conventional federated learning algorithms allow the worker to decrease the communication frequency...

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Detalhes bibliográficos
Principais autores: Jingyan Jiang, Liang Hu
Formato: Artigo
Idioma:English
Publicado em: Wiley 2020-05-01
coleção:CAAI Transactions on Intelligence Technology
Assuntos:
learning (artificial intelligence)
gradient methods
communication frequency
parameter server design
nodes-to-server bandwidths
stochastic gradient descent training
end-to-end training
real-world federated learning scenarios
adaptive partial gradient aggregation method
gradient partial level decentralised
partial gradient exchange mechanism
node-to-node bandwidth
communication time
adaptive model
training time
decentralised federated learning
machine learning model
geo-distributed workers
inherently communication
communication efficiency
conventional federated learning algorithms
Acesso em linha:https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0082

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https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0082

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