167 citations · 270 across the 10 of their papers we have counts for
16 papers
Personalized Federated Learning with Multiple Known Clusters
Boxiang Lyu, Filip Hanzely, Mladen Kolar
We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that…
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization
Mher Safaryan, Filip Hanzely, Peter Richtárik
Large scale distributed optimization has become the default tool for the training of supervised machine learning models with a large number of parameters and training data. Recent…
Local SGD: Unified Theory and New Efficient Methods
Eduard Gorbunov, Filip Hanzely, Peter Richtárik
We present a unified framework for analyzing local SGD methods in the convex and strongly convex regimes for distributed/federated training of supervised machine learning models. W…
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth +1
In this work, we consider the optimization formulation of personalized federated learning recently introduced by Hanzely and Richtárik (2020) which was shown to give an alternative…
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
Filip Hanzely
Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of dat…