3 citations · 4 across the 2 of their papers we have counts for
6 papers
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…
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…
CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization
Laurent Condat, Ivan Agarský, Peter Richtárik
In distributed optimization, a large number of machines alternate between local computations and communication with a coordinating server. Communication, which can be slow and cost…
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
Kai Yi, Georg Meinhardt, Laurent Condat +1
Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with…
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a…
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…