This repository contains the implementation of the paper: Federated Distillation of Natural Language Understanding with Confident Sinkhorns

Overview

Federated Distillation of Natural Language Understanding with Confident Sinkhorns

This repository provides an alternative method for ensembled distillation of local models to a global model. The local models can be trained via entropy or optimal transport (OT) loss. We train local (on-device) models using cross-entropy loss due to the higher computational complexity of OT. The global model is pretrained on global dataset which is relatively bigger than local datasets.

How to run?

For the Sentiment task, in the Sentiment directory

Within the dataset directory:
- Follow the folder-specific readme to download the datasets and preprocess.

Within the src directory:
- To pretrain local models: run scripts for local models mentioned in bash.sh file under the comment line #train local models.

- To pretrain global model: run the script for global model mentioned in bash.sh file under the comment line #pretrain global model.

- To create noisy labels: run the script mentioned in bash.sh file under the comment line #create noisy labels from local models on transfer set.

- To find pretrained local and global model bias: run the script mentioned in bash.sh file under the comment line #distribution bias.

- To distil knowledge from pretrained local and global model: run the script mentioned in bash.sh file under the comment line #distill knowledge.

Citation

Please cite our paper if you find this repository useful. The latest version is available here.

@article{bhardwaj2021federated,
title={Federated Distillation of Natural Language Understanding with Confident Sinkhorns},
author={Bhardwaj, Rishabh and Vaidya, Tushar and Poria, Soujanya},
journal={arXiv preprint arXiv:2110.02432},
year={2021} }

Contact

If you have any questions, please feel free to contact [email protected].

Owner
Deep Cognition and Language Research (DeCLaRe) Lab
Deep Cognition and Language Research (DeCLaRe) Lab
SwinTrack: A Simple and Strong Baseline for Transformer Tracking

SwinTrack This is the official repo for SwinTrack. A Simple and Strong Baseline Prerequisites Environment conda (recommended) conda create -y -n SwinT

LitingLin 196 Jan 04, 2023
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Gengshan Yang 157 Nov 21, 2022
[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess

RUCAIBox 26 Dec 19, 2022
CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr

HUAWEI Noah's Ark Lab 3k Jan 08, 2023
Code basis for the paper "Camera Condition Monitoring and Readjustment by means of Noise and Blur" (2021)

Camera Condition Monitoring and Readjustment by means of Noise and Blur This repository contains the source code of the paper: Wischow, M., Gallego, G

7 Dec 22, 2022
Code for Environment Inference for Invariant Learning (ICML 2020 UDL Workshop Paper)

Environment Inference for Invariant Learning This code accompanies the paper Environment Inference for Invariant Learning, which appears at ICML 2021.

Elliot Creager 40 Dec 09, 2022
Modeling CNN layers activity with Gaussian mixture model

GMM-CNN This code package implements the modeling of CNN layers activity with Gaussian mixture model and Inference Graphs visualization technique from

3 Aug 05, 2022
The "breathing k-means" algorithm with datasets and example notebooks

The Breathing K-Means Algorithm (with examples) The Breathing K-Means is an approximation algorithm for the k-means problem that (on average) is bette

Bernd Fritzke 75 Nov 17, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
Code for Dual Contrastive Learning for Unsupervised Image-to-Image Translation, NTIRE, CVPRW 2021.

arXiv Dual Contrastive Learning Adversarial Generative Networks (DCLGAN) We provide our PyTorch implementation of DCLGAN, which is a simple yet powerf

119 Dec 04, 2022
Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size.

Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size. The hub data layout enables rapid transformations and streaming of data while training m

Activeloop 5.1k Jan 08, 2023
[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer (CVPR 2022) [Paper] Akshita Gupta*, Sanath Narayan*, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Sh

Akshita Gupta 127 Dec 27, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

10 Dec 14, 2022
[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Robot Action Primitives (RAPS) This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action

Murtaza Dalal 55 Dec 27, 2022
Code for "Adversarial attack by dropping information." (ICCV 2021)

AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa

Ranjie Duan 52 Nov 10, 2022
T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time The first Lidar-only odometry framework with high performance based on tr

Pengwei Zhou 183 Dec 01, 2022
Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

2017 VQA Challenge Winner (CVPR'17 Workshop) pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challeng

Mark Dong 166 Dec 11, 2022
这是一个unet-pytorch的源码,可以训练自己的模型

Unet:U-Net: Convolutional Networks for Biomedical Image Segmentation目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Downl

Bubbliiiing 567 Jan 05, 2023
UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac protocols on unmanned aerial vehicle networks.

UAV-Networks Simulator - Autonomous Networking - A.A. 20/21 UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac pr

0 Nov 13, 2021
Multi-Glimpse Network With Python

Multi-Glimpse Network Our code requires Python ≥ 3.8 Installation For example, venv + pip: $ python3 -m venv env $ source env/bin/activate (env) $ pyt

9 May 10, 2022