351 citations · 353 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 351 cited
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami +1
Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately a…
cs.LG2018★ 2 cited
Model Aggregation via Good-Enough Model Spaces
Neel Guha, Virginia Smith
In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to communication, privacy,…