346 papers with code • 0 benchmarks • 7 datasets
Federated Learning is a framework to train a centralized model for a task where the data is de-centralized across different devices/ silos.
This helps preserve privacy of data on various devices as only the weight updates are shared with the centralized model so the data can remain on each device and we can still train a model using that data.
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning
Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones.
Federated learning (FL) is a rapidly growing research field in machine learning.
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
However, in many social network scenarios, centralized federated learning is not applicable (e. g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable).
Training deep neural networks on large datasets can often be accelerated by using multiple compute nodes.
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.