System and method for federated learning with local differential privacy

Patent metadata

Field	Value
Publication number	US-12373729-B2
Application number	US-202017085699-A
Country	US
Kind code	B2
Filing date	Oct 30, 2020
Priority date	May 28, 2020
Publication date	Jul 29, 2025
Grant date	Jul 29, 2025

Abstract

In one embodiment, a method includes accessing a plurality of initial gradients associated with a machine-learning model from a data store associated with a first electronic device, selecting one or more of the plurality of initial gradients for perturbation, generating one or more perturbed gradients for the one or more selected initial gradients based on a gradient-perturbation model, respectively, wherein for each selected initial gradient: an input to the gradient-perturbation model comprises the selected initial gradient having a value x, the gradient-perturbation model changes x into a first continuous value with a first probability or a second continuous value with a second probability, and the first and second probabilities are determined based on x, and sending the one or more perturbed gradients from the first electronic device to a second electronic device.

First claim

What is claimed is:

1. A method comprising, by one or more processors of a first electronic device:
by one or more of the processors, accessing, from a data store associated with the first electronic device, a plurality of initial gradients that determine the output of a machine-learning model for a given input;
by one or more of the processors, selecting one or more of the plurality of initial gradients for perturbation;
by one or more of the processors, obfuscating user-identifying information of a user corresponding to the plurality of initial gradients by generating, based on a gradient-perturbation model, one or more perturbed gradients for the one or more selected initial gradients, respectively, wherein for each selected initial gradient:
an input to the gradient-perturbation model comprises the selected initial gradient having a value x,
the gradient-perturbation model perturbs the value x into either of two endpoints of a perturbation range, the two endpoints comprising (1) a first continuous value,
comprising a lower boundary of the perturbation range defined by a center value c and a distance r from the center c, with a first probability and (2) a second continuous value, comprising an upper boundary of the perturbation range, with a second probability, wherein (1) the first and second probabilities are determined based on x, (2) the first and second probabilities sum to 1, and (3) the first and second probabilities preserve accuracy when averaging initial gradients by making the expectation value of each particular perturbed gradient, E (A (x)), equal to its corresponding initial gradient x, even though the particular perturbed gradient is not equal to the corresponding initial gradient; and

by one or more of the processors, sending, from the first electronic device to a second electronic device, the one or more perturbed gradients.

2. The method of claim 1 , further comprising:
determining, based on one or more privacy policies, that one or more of the plurality of initial gradients should be perturbed.

3. The method of claim 1 , further comprising:
receiving, at the first electronic device from the second electronic device, a plurality of weights of the machine-learning model, wherein the plurality of weights are determined based on the one or more perturbed gradients; and
determining, by the first electronic device, a plurality of new gradients for the plurality of weights.

4. The method of claim 1 , wherein the perturbation of the one or more selected gradients is performed according to:

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Assignees

• Samsung Electronics Co Ltd
• Samsung Electronics Co Ltd

Inventors

• Qian Jianwei
• Sun Lichao
• Chen Xun

Classifications

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06F21/6263 Primary

  during internet communication, e.g. revealing personal data from cookies · CPC title