3 citations · 10 across the 14 of their papers we have counts for
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FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences
Artem Agafonov, Brahim Erraji, Martin Takáč
In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning Second-Order Framework via Compression and Sketching), the second-order framework FLECS was proposed for the…
Exploiting higher-order derivatives in convex optimization methods
Dmitry Kamzolov, Alexander Gasnikov, Pavel Dvurechensky +2
Exploiting higher-order derivatives in convex optimization is known at least since 1970's. In each iteration higher-order (also called tensor) methods minimize a regularized Taylor…
FLECS: A Federated Learning Second-Order Framework via Compression and Sketching
Artem Agafonov, Dmitry Kamzolov, Rachael Tappenden +2
Inspired by the recent work FedNL (Safaryan et al, FedNL: Making Newton-Type Methods Applicable to Federated Learning), we propose a new communication efficient second-order framew…