112 citations · 139 across the 8 of their papers we have counts for
5 papers · 1 filter
Adversarial training in communication constrained federated learning
Devansh Shah, Parijat Dube, Supriyo Chakraborty +1
Federated learning enables model training over a distributed corpus of agent data. However, the trained model is vulnerable to adversarial examples, designed to elicit misclassific…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Improving the affordability of robustness training for DNNs
Sidharth Gupta, Parijat Dube, Ashish Verma
Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational…
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal, Anamitra R. Choudhury, Saurabh M. Raje +3
We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pert…
A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs
Koyel Mukherjee, Alind Khare, Ashish Verma
Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training o…