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Found 8 papers, 4 papers with code
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train XGBoost in the context of federated learning (FL).
Secure Aggregation for Federated Learning in Flower
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 server.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
On-device Federated Learning with Flower
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 machine learning from the need to store data in the cloud.
A first look into the carbon footprint of federated learning
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?
Then, we compare the carbon footprint of FL to traditional centralized learning.
Flower: A Friendly Federated Learning Research Framework
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, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
