36 citations · 130 across the 23 of their papers we have counts for
4 papers · 1 filter
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
Luyao Guo, Sulaiman A. Alghunaim, Kun Yuan +2
The ProxSkip algorithm for distributed optimization is gaining increasing attention due to its effectiveness in reducing communication. However, existing analyses of ProxSkip are l…
Near-Linear Time Projection onto the Ball; Application to Sparse Autoencoders
Guillaume Perez, Laurent Condat, Michel Barlaud
Looking for sparsity is nowadays crucial to speed up the training of large-scale neural networks. Projections onto the and are among the most efficie…
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
Kai Yi, Laurent Condat, Peter Richtárik
Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication wi…
TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
Laurent Condat, Ivan Agarský, Grigory Malinovsky +1
In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate t…