242 citations · 647 across the 20 of their papers we have counts for
3 papers · 1 filter
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…
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
Filip Hanzely, Boxin Zhao, Mladen Kolar
We investigate the optimization aspects of personalized Federated Learning (FL). We propose general optimizers that can be applied to numerous existing personalized FL objectives,…