3 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Byzantine-Tolerant Methods for Distributed Variational Inequalities
Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu +4
Robustness to Byzantine attacks is a necessity for various distributed training scenarios. When the training reduces to the process of solving a minimization problem, Byzantine rob…
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.…
Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees
Jihao Xin, Marco Canini, Peter Richtárik +1
Distributed training enables large-scale deep learning, but suffers from high communication overhead, especially as models and datasets grow. Gradient compression, particularly qua…