1.7k citations · 1.9k across the 25 of their papers we have counts for
9 papers · 1 filter
On the Convergence of FedProx with Extrapolation and Inexact Prox
Hanmin Li, Peter Richtárik
Enhancing the FedProx federated learning algorithm (Li et al., 2020) with server-side extrapolation, Li et al. (2024a) recently introduced the FedExProx method. Their theoretical a…
Error Feedback Shines when Features are Rare
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
We provide the first proof that gradient descent with greedy sparsification and error feedback $\left({\…
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity
Xun Qian, Hanze Dong, Tong Zhang +1
Communication overhead is well known to be a key bottleneck in large scale distributed learning, and a particularly successful class of methods which help to overcome this bottlene…
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes
Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik
In this work, we consider the problem of minimizing the sum of Moreau envelopes of given functions, which has previously appeared in the context of meta-learning and personalized f…
AIDE: Fast and Communication Efficient Distributed Optimization
Sashank J. Reddi, Jakub Konečný, Peter Richtárik +2
In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE alg…
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation
Zheng Qu, Peter Richtárik
The design and complexity analysis of randomized coordinate descent methods, and in particular of variants which update a random subset (sampling) of coordinates in each iteration,…