514 citations · 918 across the 31 of their papers we have counts for
6 papers · 1 filter
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
Xinchi Qiu, Javier Fernandez-Marques, Pedro PB Gusmao +3
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training qualit…
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…
A first look into the carbon footprint of federated learning
Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques +6
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.…
Can Federated Learning Save The Planet?
Xinchi Qiu, Titouan Parcollet, Daniel J. Beutel +3
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.…
Flower: A Friendly Federated Learning Research Framework
Daniel J. Beutel, Taner Topal, Akhil Mathur +8
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, t…
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition
Yan Gao, Titouan Parcollet, Nicholas Lane
Knowledge distillation has been widely used to compress existing deep learning models while preserving the performance on a wide range of applications. In the specific context of A…