Search Results for author:
Found 2 papers, 1 papers with code
Budgeted Online Model Selection and Fine-Tuning via Federated Learning
Although employing a larger set of candidate models naturally leads to more flexibility in model selection, this may be infeasible in cases where prediction tasks are performed on edge devices with limited memory.
Personalized Online Federated Learning with Multiple Kernels
Federated learning enables a group of learners (called clients) to train an MKL model on the data distributed among clients to perform online non-linear function approximation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
