5 citations · 6 across the 19 of their papers we have counts for
3 papers · 2 filters
Who to Trust? Aggregating Client Predictions in Federated Distillation
Viktor Kovalchuk, Denis Son, Arman Bolatov +6
Under data heterogeneity (e.g., ), clients may produce unreliable predictions for instances belonging to unfamiliar classes. An equally weighted combinatio…
Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level
Saleh Vatan Khah, Savelii Chezhegov, Shahrokh Farahmand +2
Gradient clipping is a fundamental tool in Deep Learning, improving the high-probability convergence of stochastic first-order methods like SGD, AdaGrad, and Adam under heavy-taile…
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy
Rustem Islamov, Samuel Horvath, Aurelien Lucchi +2
Strong Differential Privacy (DP) and Optimization guarantees are two desirable properties for a method in Federated Learning (FL). However, existing algorithms do not achieve both…