12 citations · 21 across the 20 of their papers we have counts for
3 papers · 2 filters
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms
Rustem Islamov, Mher Safaryan, Dan Alistarh
We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting, where each worker has its own computation and communication speeds, as well as data distri…
Clip21: Error Feedback for Gradient Clipping
Sarit Khirirat, Eduard Gorbunov, Samuel Horváth +3
Motivated by the increasing popularity and importance of large-scale training under differential privacy (DP) constraints, we study distributed gradient methods with gradient clipp…
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity
Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov +1
We present a partially personalized formulation of Federated Learning (FL) that strikes a balance between the flexibility of personalization and cooperativeness of global training.…