Federated Learning

287 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.

Latest papers with code

OpenFed: An Open-Source Security and Privacy Guaranteed Federated Learning Framework

federallab/openfed 16 Sep 2021

The broad application of artificial intelligence techniques ranging from self-driving vehicles to advanced medical diagnostics afford many benefits.

Federated Learning

16 Sep 2021

Source Inference Attacks in Federated Learning

hongshenghu/source-inference-fl 13 Sep 2021

However, existing MIAs ignore the source of a training member, i. e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients.

Federated Learning Inference Attack

13 Sep 2021

GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning

liuzelei13/gtg-shapley 5 Sep 2021

In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required, through extensive experiments under diverse realistic data distribution settings.

Federated Learning

05 Sep 2021

Federated Multi-Task Learning under a Mixture of Distributions

omarfoq/fedem 23 Aug 2021

The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models.

Fairness Federated Learning +1

23 Aug 2021

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

morningd/flexcfl 22 Aug 2021

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally.

Federated Learning

22 Aug 2021

FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

buaa-ci-lab/fedskel 20 Aug 2021

Federated learning aims to protect users' privacy while performing data analysis from different participants.

Federated Learning

20 Aug 2021

Multi-Center Federated Learning

mingxuts/multi-center-fed-learning 19 Aug 2021

By comparison, a mixture of multiple global models could capture the heterogeneity across various users if assigning the users to different global models (i. e., centers) in FL.

Federated Learning

19 Aug 2021

Aggregation Delayed Federated Learning

y-xue/radfed 17 Aug 2021

Federated learning is a distributed machine learning paradigm where multiple data owners (clients) collaboratively train one machine learning model while keeping data on their own devices.

Federated Learning

17 Aug 2021

FedMatch: Federated Learning Over Heterogeneous Question Answering Data

chriskuei/fedmatch 11 Aug 2021

A possible solution to this dilemma is a new approach known as federated learning, which is a privacy-preserving machine learning technique over distributed datasets.

Federated Learning Question Answering

11 Aug 2021

The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset

zhzhang2018/decentralizedfl 7 Aug 2021

Federated Learning is an algorithm suited for training models on decentralized data, but the requirement of a central "server" node is a bottleneck.

Federated Learning

07 Aug 2021