Search Results for author:
Found 2 papers, 1 papers with code
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints
The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets.
Communication-Efficient Federated Learning via Optimal Client Sampling
The central server collects updated local models from only the selected clients and combines them with estimated model updates of the clients that were not selected for communication.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
