Self-adaptive asynchronous federated learning method with local privacy protection
The invention discloses a self-adaptive asynchronous federal learning method with local privacy protection, which comprises the following steps that: a central server initializes a global model and broadcasts global model parameters, a gradient cutting standard, a noise mechanism and a noise varianc...
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Format: | Patent |
Sprache: | chi ; eng |
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Zusammenfassung: | The invention discloses a self-adaptive asynchronous federal learning method with local privacy protection, which comprises the following steps that: a central server initializes a global model and broadcasts global model parameters, a gradient cutting standard, a noise mechanism and a noise variance to all participating users; each user firstly uses samples extracted from local data to train a global model and cuts and disturbs the gradients one by one, then the disturbed gradients are sent to a central server, the central server selects the first K disturbed gradients from a buffer queue to perform average aggregation, the averaged gradient is substituted into a stochastic gradient descent formula to update global model parameters, and a gradient cutting standard, a noise variance and a learning rate are adaptively adjusted according to the number of iterations in a preset stage; and then the central server broadcasts the updated global model parameters, the gradient cutting standard and the noise variance |
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