158 citations · 158 across the 2 of their papers we have counts for
3 papers · 1 filter
Differentially Private Meta-Learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas +1
Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning. However,…
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan +1
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue,…
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu +5
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can he…