17 citations · 30 across the 7 of their papers we have counts for
3 papers · 1 filter
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
Kai Yi, Nidham Gazagnadou, Peter Richtárik +1
The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. Th…
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space
Yuyang Deng, Nidham Gazagnadou, Junyuan Hong +2
Recent studies demonstrated that the adversarially robust learning under attack is harder to generalize to different domains than standard domain adaptation. How to t…
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes
Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou +1
Tuning the step size of stochastic gradient descent is tedious and error prone. This has motivated the development of methods that automatically adapt the step size using readily a…