167 citations · 325 across the 3 of their papers we have counts for
15 papers
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…
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…
Learning from History for Byzantine Robust Optimization
Sai Praneeth Karimireddy, Lie He, Martin Jaggi
Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing…
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
Lossy gradient compression has become a practical tool to overcome the communication bottleneck in centrally coordinated distributed training of machine learning models. However, a…
Secure Byzantine-Robust Machine Learning
Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentra…