Federated Learning

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.

Greatest papers with code

SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning

FederatedAI/FATE 21 Oct 2021

Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.

Federated Learning

Generative Models for Effective ML on Private, Decentralized Datasets

tensorflow/federated ICLR 2020

To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.

Federated Learning

Federated Learning for Mobile Keyboard Prediction

tensorflow/federated 8 Nov 2018

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 Language Modelling

Advances and Open Problems in Federated Learning

FedML-AI/FedML 10 Dec 2019

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.

Federated Learning

Central Server Free Federated Learning over Single-sided Trust Social Networks

FedML-AI/FedML 11 Oct 2019

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).

Federated Learning

Label Leakage and Protection in Two-party Split Learning

bytedance/fedlearner 17 Feb 2021

Two-party split learning is a popular technique for learning a model across feature-partitioned data.

Federated Learning

Flower: A Friendly Federated Learning Research Framework

adap/flower 28 Jul 2020

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.

Federated Learning