SERVER EFFICIENT ENHANCEMENT OF PRIVACY IN FEDERATED LEARNING

Techniques are disclosed that enable training a global model using gradients provided to a remote system by a set of client devices during a reporting window, where each client device randomly determines a reporting time in the reporting window to provide the gradient to the remote system. Various i...

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Hauptverfasser:	THAKURTA, Abhradeep Guha, KAIROUZ, Peter, THAKKAR, Om, DE BALLE PIGEM, Borja, MCMAHAN, Brendan
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS
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creator	THAKURTA, Abhradeep Guha KAIROUZ, Peter THAKKAR, Om DE BALLE PIGEM, Borja MCMAHAN, Brendan
description	Techniques are disclosed that enable training a global model using gradients provided to a remote system by a set of client devices during a reporting window, where each client device randomly determines a reporting time in the reporting window to provide the gradient to the remote system. Various implementations include each client device determining a corresponding gradient by processing data using a local model stored locally at the client device, where the local model corresponds to the global model.
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subjects	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS
title	SERVER EFFICIENT ENHANCEMENT OF PRIVACY IN FEDERATED LEARNING
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