36 citations · 130 across the 23 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
Laurent Condat, Kai Yi, Peter Richtárik
In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce…
Optimal Gradient Compression for Distributed and Federated Learning
Alyazeed Albasyoni, Mher Safaryan, Laurent Condat +1
Communicating information, like gradient vectors, between computing nodes in distributed and federated learning is typically an unavoidable burden, resulting in scalability issues.…
From Local SGD to Local Fixed-Point Methods for Federated Learning
Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov +2
Most algorithms for solving optimization problems or finding saddle points of convex-concave functions are fixed-point algorithms. In this work we consider the generic problem of f…
On-the-fly Approximation of Multivariate Total Variation Minimization
Jordan Frecon, Nelly Pustelnik, Patrice Abry +1
In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for u…