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Xu, J. (author), Hong, C. (author), Huang, J. (author), Chen, Lydia Y. (author), Decouchant, Jérémie (author)Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model. Depending on the aggregation method used, the local updates are either the gradients or the weights of local learning models, e.g., FedAvg...conference paper 2023
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Cox, B.A. (author), Chen, Lydia Y. (author), Decouchant, Jérémie (author)Federated Learning (FL) is a popular deep learning approach that prevents centralizing large amounts of data, and instead relies on clients that update a global model using their local datasets. Classical FL algorithms use a central federator that, for each training round, waits for all clients to send their model updates before aggregating them...conference paper 2022