167 citations · 640 across the 13 of their papers we have counts for
17 papers · 1 filter
Correlated quantization for distributed mean estimation and optimization
Ananda Theertha Suresh, Ziteng Sun, Jae Hun Ro +1
We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error…
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
Jianyu Wang, Hang Qi, Ankit Singh Rawat +4
In classical federated learning, the clients contribute to the overall training by communicating local updates for the underlying model on their private data to a coordinating serv…
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…
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum +4
Negative sampling schemes enable efficient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all lab…
Learning discrete distributions: user vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Yu +2
Much of the literature on differential privacy focuses on item-level privacy, where loosely speaking, the goal is to provide privacy per item or training example. However, recently…
Self-supervised Learning for Large-scale Item Recommendations
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng +8
Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with…