Federated Learning D
Aalto University
<p>* machine learning basics: data, model, loss, regularization, multi-task learning, semi-supervised learning, transfer learning</p><p>* network basics: graphs and their matrices, community/cluster structure </p><p>* TV minimization as a flexible design principle for FL</p><p>* main flavors of FL (centralized, clustered, personalized) as special cases of TV minimization</p><p>* distributed optimization: models for distributed computation, fixed-point iterations, gradient-based methods</p>
Back
|
|
