Method and system for federated adversarial domain adaptation

A system of a client device comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors and configured with instructions executable by the one or more processors to cause the system to perform operations comprising:

• receiving, from a server device, model parameters of a global machine learning model, wherein the client device is one of a plurality client devices that receive the model parameters from the server device;
• constructing a local machine learning model based on the model parameters of the global machine learning model, wherein the local machine learning model comprises two branches corresponding to two loss functions;
• training the local machine learning model based on local training data, wherein the training comprises updating the model parameters to minimize a first loss function of the two loss functions and maximize a second loss function of the two loss functions at a same time;
• sending the updated model parameters back to the server device to cause the server device to update parameters of the global machine learning model based on the updated model parameters from the client device and parameters received from other client devices of the plurality of client devices;
• receiving, from the server device, model parameters of an updated global machine learning model;
• and
• performing another round of local training based on the received model parameters of the updated global machine learning model.

A non-transitory computer-readable storage medium of a client device, configured with instructions executable by one or more processors to cause the one or more processors to perform operations comprising:

• receiving, from a server device, model parameters of a global machine learning model, wherein the client device is one of a plurality client devices that receive the model parameters from the server device;
• constructing a local machine learning model based on the model parameters of the global machine learning model, wherein the local machine learning model comprises two branches corresponding to two loss functions;
• training the local machine learning model based on local training data, wherein the training comprises updating the model parameters to minimize a first loss function of the two loss functions and maximize a second loss function of the two loss functions at a same time;
• sending the updated model parameters back to the server device to cause the server device to update parameters of the global machine learning model based on the updated model parameters from the client device and parameters received from other client devices of the plurality of client devices;
• receiving, from the server device, model parameters of an updated global machine learning model;
• and
• performing another round of local training based on the received model parameters of the updated global machine learning model.