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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Veröffentlicht in:	CAAI Transactions on Intelligence Technology 2020-09, Vol.5 (3), p.230-236
Hauptverfasser:	Jiang, Jingyan, Hu, Liang
Format:	Artikel
Sprache:	eng
Schlagworte:	adaptive model adaptive partial gradient aggregation method Algorithms Bandwidths Communication communication efficiency communication frequency communication time conventional federated learning algorithms Convergence decentralised federated learning Design parameters end-to-end training Federated learning geo-distributed workers gradient methods gradient partial level decentralised inherently communication learning (artificial intelligence) Machine learning machine learning model Methods Model updating Network latency node-to-node bandwidth nodes-to-server bandwidths parameter server design partial gradient exchange mechanism real-world federated learning scenarios Research Article stochastic gradient descent training Topology training time Wide area networks
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