132 citations · 177 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…