35 citations · 88 across the 18 of their papers we have counts for
4 papers · 1 filter
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…
Beyond the Ideal: Analyzing the Inexact Muon Update
Egor Shulgin, Sultan AlRashed, Francesco Orabona +1
The Muon optimizer has rapidly emerged as a powerful, geometry-aware alternative to AdamW, demonstrating strong performance in large-scale training of neural networks. However, a c…
Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)
Artem Riabinin, Egor Shulgin, Kaja Gruntkowska +1
Recent developments in deep learning optimization have brought about radically new algorithms based on the Linear Minimization Oracle (LMO) framework, such as and $\sf S…
Smoothed Normalization for Efficient Distributed Private Optimization
Egor Shulgin, Sarit Khirirat, Peter Richtárik
Federated learning enables training machine learning models while preserving the privacy of participants. Surprisingly, there is no differentially private distributed method for sm…