Federated Learning

671 papers with code • 12 benchmarks • 8 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.

Libraries

Use these libraries to find Federated Learning models and implementations

Most implemented papers

Federated Optimization in Heterogeneous Networks

litian96/FedProx 14 Dec 2018

Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity).

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.

Agnostic Federated Learning

litian96/fair_flearn 1 Feb 2019

A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning 13 Sep 2019

In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning.

Inverting Gradients -- How easy is it to break privacy in federated learning?

JonasGeiping/breaching 31 Mar 2020

The idea of federated learning is to collaboratively train a neural network on a server.

Differentially Private Federated Learning: A Client Level Perspective

cyrusgeyer/DiffPrivate_FedLearning ICLR 2019

In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model.

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.

LEAF: A Benchmark for Federated Settings

TalwalkarLab/leaf 3 Dec 2018

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.

Adaptive Personalized Federated Learning

MLOPTPSU/FedTorch 30 Mar 2020

Investigation of the degree of personalization in federated learning algorithms has shown that only maximizing the performance of the global model will confine the capacity of the local models to personalize.

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.