514 citations · 712 across the 12 of their papers we have counts for
5 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.…
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…