732 citations · 1.7k across the 44 of their papers we have counts for
6 papers · 1 filter
Linear Speedup in Personalized Collaborative Learning
El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich +2
Collaborative training can improve the accuracy of a model for a user by trading off the model's bias (introduced by using data from other users who are potentially different) agai…
Optimal Model Averaging: Towards Personalized Collaborative Learning
Felix Grimberg, Mary-Anne Hartley, Sai P. Karimireddy +1
In federated learning, differences in the data or objectives between the participating nodes motivate approaches to train a personalized machine learning model for each node. One s…
Towards Model Agnostic Federated Learning Using Knowledge Distillation
Andrei Afonin, Sai Praneeth Karimireddy
Is it possible to design an universal API for federated learning using which an ad-hoc group of data-holders (agents) collaborate with each other and perform federated learning? Su…
RelaySum for Decentralized Deep Learning on Heterogeneous Data
Thijs Vogels, Lie He, Anastasia Koloskova +4
In decentralized machine learning, workers compute model updates on their local data. Because the workers only communicate with few neighbors without central coordination, these up…
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…
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich +1
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of het…