Feb 17, 2016 · We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation.
Abstract. Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems.
People also ask
What is a federated learning research paper?
What is a federated learning example?
What are the three types of federated learning?
Is federated learning a hot topic?
Nov 11, 2022 · This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in ...
Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their ...
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model with training data distributed over a large ...
May 1, 2020 · Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data ...
This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and ...
Federated learning is a sub-field of machine learning focusing on settings in which multiple entities collaboratively train a model while ensuring that ...
Decentralized federated learning through proxy model sharing - Nature
www.nature.com › ... › articles
May 22, 2023 · In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning.
Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized and private.