1 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques +19
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Exi…
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…
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.…