Abstract: Methods and devices are provided for performing federated learning. A global model is distributed from a server to a plurality of client devices. At each of the plurality of client devices: model inversion is performed on the global model to generate synthetic data; the global model is on an augmented dataset of collected data and the synthetic data to generate a respective client model; and the respective client model is transmitted to the server. At the server: client models are received from the plurality of client devices, where each client model is received from a respective client device of the plurality of client devices: model inversion is performed on each client model to generate a synthetic dataset; the client models are averaged to generate an averaged model; and the averaged model is trained using the synthetic dataset to generate an updated model.

Abstract: A federated machine-learning system includes a global server and client devices. The server receives updates of weight factor dictionaries and factor strengths vectors from the clients, and generates a globally updated weight factor dictionary and a globally updated factor strengths vector. A client device selects a group of parameters from a global group of parameters, and trains a model using a dataset of the client device and the group of selected parameters. The client device sends to the server a client-updated weight factor dictionary and a client-updated factor strengths vector. The client device receives the globally updated weight factor dictionary and the globally updated factor strengths vector, and retrains the model using the dataset of the client device, the group of parameters selected by the client device, and the globally updated weight factor dictionary and the globally updated factor strengths vector.