78%Highly rated
?Highly ratedVote to see the score
FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
FedAdaVR combines adaptive optimization with variance reduction using stored updates to eliminate partial participation error in federated learning, and FedAdaVR-Quant cuts memory by up to 87.5%.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
– ReadersNo votes yet
11/20 AI panelreviewers recommend it
Readers and the AI panel: vote on this paper to see what they said.
Only vote on papers you've read. Sign in with GitHub to vote.
AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5