13 citations · 57 across the 29 of their papers we have counts for
29 papers
Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
Grigory Malinovsky
Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications. Modern large-scale t…
Distance-Aware Muon: Adaptive Step Scaling for Normalized Optimization
Yury Demidovich, Abhishek Chakraborty, Grigory Malinovsky +2
Muon and related normalized optimizers decouple the choice of update direction from the choice of step scale, but their practical performance remains sensitive to the scale of the…
Communication-Efficient Gluon in Federated Learning
Xun Qian, Alexander Gaponov, Grigory Malinovsky +1
Recent developments have shown that Muon-type optimizers based on linear minimization oracles (LMOs) over non-Euclidean norm balls have the potential to get superior practical perf…
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
Rustem Islamov, Grigory Malinovsky, Alexander Gaponov +3
Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy. However, g…
First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
Egor Shulgin, Grigory Malinovsky, Sarit Khirirat +1
Federated Learning (FL) enables collaborative training on decentralized data. Differential privacy (DP) is crucial for FL, but current private methods often rely on unrealistic ass…
Improved Convergence in Parameter-Agnostic Error Feedback through Momentum
Abdurakhmon Sadiev, Yury Demidovich, Igor Sokolov +3
Communication compression is essential for scalable distributed training of modern machine learning models, but it often degrades convergence due to the noise it introduces. Error…