167 citations · 439 across the 10 of their papers we have counts for
10 papers
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Samuel Horvath, Stefanos Laskaridis, Shashank Rajput +1
Deep Neural Networks (DNNs) have been a large driver for AI breakthroughs in recent years. However, these models have been getting increasingly large as they become more accurate a…
Balancing Privacy and Performance for Private Federated Learning Algorithms
Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub +1
Federated learning (FL) is a distributed machine learning (ML) framework where multiple clients collaborate to train a model without exposing their private data. FL involves cycles…
Granger Causality using Neural Networks
Malik Shahid Sultan, Samuel Horvath, Hernando Ombao
Dependence between nodes in a network is an important concept that pervades many areas including finance, politics, sociology, genomics and the brain sciences. One way to character…
FL_PyTorch: optimization research simulator for federated learning
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared machine learning model while keeping training data locally on the de…
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…
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida +3
Federated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogenei…
Optimal Client Sampling for Federated Learning
Wenlin Chen, Samuel Horvath, Peter Richtarik
It is well understood that client-master communication can be a primary bottleneck in Federated Learning. In this work, we address this issue with a novel client subsampling scheme…
Natural Compression for Distributed Deep Learning
Samuel Horvath, Chen-Yu Ho, Ludovit Horvath +3
Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among…
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
Dmitry Kovalev, Samuel Horvath, Peter Richtarik
The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few ye…
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
Samuel Horváth, Peter Richtárik
We provide the first importance sampling variants of variance reduced algorithms for empirical risk minimization with non-convex loss functions. In particular, we analyze non-conve…