132 citations · 177 across the 13 of their papers we have counts for
13 papers
Efficient Vertical Federated Learning with Secure Aggregation
Xinchi Qiu, Heng Pan, Wanru Zhao +3
The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can trai…
Protea: Client Profiling within Federated Systems using Flower
Wanru Zhao, Xinchi Qiu, Javier Fernandez-Marques +2
Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While…
Secure Aggregation for Federated Learning in Flower
Kwing Hei Li, Pedro Porto Buarque de Gusmão, Daniel J. Beutel +1
Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the…
On-device Federated Learning with Flower
Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão +6
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do…
End-to-End Speech Recognition from Federated Acoustic Models
Yan Gao, Titouan Parcollet, Salah Zaiem +4
Training Automatic Speech Recognition (ASR) models under federated learning (FL) settings has attracted a lot of attention recently. However, the FL scenarios often presented in th…
Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Pedro P. B. de Gusmao +3
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly de…
milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion
Chris Xiaoxuan Lu, Muhamad Risqi U. Saputra, Peijun Zhao +6
Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmente…
SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation
Yasin Almalioglu, Mehmet Turan, Alp Eren Sari +4
In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimat…
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao +5
Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have br…
Distilling Knowledge From a Deep Pose Regressor Network
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Yasin Almalioglu +2
This paper presents a novel method to distill knowledge from a deep pose regressor network for efficient Visual Odometry (VO). Standard distillation relies on "dark knowledge" for…
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning
Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Sen Wang +2
Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data. In this paper, we stud…
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
Yasin Almalioglu, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao +2
In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled da…
Fast Training of Convolutional Neural Networks via Kernel Rescaling
Pedro Porto Buarque de Gusmão, Gianluca Francini, Skjalg Lepsøy +1
Training deep Convolutional Neural Networks (CNN) is a time consuming task that may take weeks to complete. In this article we propose a novel, theoretically founded method for red…