Federated Learning

268 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

A generic framework for privacy preserving deep learning

OpenMined/PySyft 9 Nov 2018

We detail a new framework for privacy preserving deep learning and discuss its assets.

Federated Learning Privacy Preserving Deep 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

Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices

learning-at-home/hivemind 4 Mar 2021

Training deep neural networks on large datasets can often be accelerated by using multiple compute nodes.

Distributed Optimization Federated Learning

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

We first show that, norm attack, a simple method that uses the norm of the communicated gradients between the parties, can largely reveal the ground-truth labels from the participants.

Federated Learning

Learning Private Neural Language Modeling with Attentive Aggregation

shaoxiongji/federated-learning 17 Dec 2018

Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.

Federated Learning Language Modelling