275 citations · 454 across the 4 of their papers we have counts for
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cs.LG2019★ 21 cited
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,…
cs.LG2019★ 158 cited
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,…