167 citations · 537 across the 11 of their papers we have counts for
22 papers
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…
HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation
Wittawat Jitkrittum, Michal Lukasik, Ananda Theertha Suresh +2
Multi-party computation (MPC) is a branch of cryptography where multiple non-colluding parties execute a well designed protocol to securely compute a function. With the non-colludi…
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…
Modifying Memories in Transformer Models
Chen Zhu, Ankit Singh Rawat, Manzil Zaheer +4
Large Transformer models have achieved impressive performance in many natural language tasks. In particular, Transformer based language models have been shown to have great capabil…
Semantic Label Smoothing for Sequence to Sequence Problems
Michal Lukasik, Himanshu Jain, Aditya Krishna Menon +4
Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising. However, extending such method…