341 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institut Polytechnique de ParisFR3 papers
- Los Alamos National LaboratoryUS3 papers
- Télécom SudParisFR3 papers
- ARM (United Kingdom)GB2 papers
- Intel (United Kingdom)GB2 papers
- Stanford UniversityUS2 papers
- Stony Brook UniversityUS2 papers
- The University of Texas at AustinUS2 papers
- Universitat Politècnica de CatalunyaES2 papers
- University of California San DiegoUS2 papers
- University of CambridgeGB2 papers
- University of RochesterUS2 papers
7 papers · 1 filter
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan +5
Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deployi…
Combining Individual and Joint Networking Behavior for Intelligent IoT Analytics
Jeya Vikranth Jeyakumar, Ludmila Cherkasova, Saina Lajevardi +4
The IoT vision of a trillion connected devices over the next decade requires reliable end-to-end connectivity and automated device management platforms. While we have seen successf…
Federated Learning Based on Dynamic Regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro +3
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen device…
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Lorena Qendro, Sangwon Ha, René de Jong +1
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerabl…
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks
Martin Ferianc, Partha Maji, Matthew Mattina +1
Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quanti…
Fast IR Drop Estimation with Machine Learning
Zhiyao Xie, Hai Li, Xiaoqing Xu +2
IR drop constraint is a fundamental requirement enforced in almost all chip designs. However, its evaluation takes a long time, and mitigation techniques for fixing violations may…
Learning low-precision neural networks without Straight-Through Estimator(STE)
Zhi-Gang Liu, Matthew Mattina
The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understandi…