21 papers
Convergence Analysis of the ProbAbilistic Gradient Estimator Algorithm for Weakly Convex Finite-Sum Optimization
Laurent Condat, Peter Richtárik
The ProbAbilistic Gradient Estimator algorithm (PAGE), a stochastic algorithm introduced by Li et al. in 2021, was designed to find stationary points for the average of smooth nonc…
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…
Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
Zhirayr Tovmasyan, Grigory Malinovsky, Laurent Condat +1
The growing prevalence of nonsmooth optimization problems in machine learning has spurred significant interest in generalized smoothness assumptions. Among these, the (L0, L1)-smoo…
Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
Artavazd Maranjyan, Peter Richtárik
Asynchronous stochastic gradient methods are central to scalable distributed optimization, particularly when devices differ in computational capabilities. Such settings arise natur…
BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
Slow and costly communication is often the main bottleneck in distributed optimization, especially in federated learning where it occurs over wireless networks. We introduce BiCoLo…
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…