36 citations · 130 across the 23 of their papers we have counts for
4 papers · 1 filter
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.…
Distributed Proximal Splitting Algorithms with Rates and Acceleration
Laurent Condat, Grigory Malinovsky, Peter Richtárik
We analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new rate…
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms
Adil Salim, Laurent Condat, Konstantin Mishchenko +1
We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nons…
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…