Federated Learning D

Aalto University

* machine learning basics: data, model, loss, regularization, multi-task learning, semi-supervised learning, transfer learning

* network basics: graphs and their matrices, community/cluster structure  

* TV minimization as a flexible design principle for FL

* main flavors of FL (centralized, clustered, personalized) as special cases of TV minimization

* distributed optimization: models for distributed computation, fixed-point iterations, gradient-based methods

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