Feb 1, 2021 · Gossip learning is a decentralized alternative to federated learning that does not require an aggregation server or indeed any central component. The natural hypothesis is that gossip learning is strictly less efficient than federated learning due to relying on a more basic infrastructure: only message passing and no cloud resources.

Jun 6, 2019 · Gossip Learning is a method for learning models from fully distributed data without central control. Each node k runs Algorithm 3. First, the node initializes a local model \(w_k\) (and its age \(t_k\) ).

Gossip learning (GL), as a decentralized learning algorithm, is an alternative to FL. GL can overlay the Mobile Ad-Hoc Network (MANET) or the Flying Ad-Hoc Network (FANET) to enable decentralized training in an infrastructure-less net-work layout based on …

Abstract—The growing computational demands of model train-ing tasks and the increased privacy awareness of consumers call for the development of new techniques in the area of machine learning. Fully decentralized approaches have been proposed, but are still in …

Sep 7, 2011 · Here we propose gossip learning, a generic approach that is based on multiple models taking random walks over the network in parallel, while applying an online learning algorithm to improve themselves, and getting combined via ensemble learning methods.

This paper identifies the conditions in which gossip learning can and cannot be applied, and introduces extensions that mitigate some of its limitations. Published in: 2019 IEEE International Conference on Big Data (Big Data)

Feb 1, 2021 · Gossip learning is a decentralized alternative to federated learning that does not require an aggregation server or indeed any central component. The natural...

Jan 17, 2024 · Gossip learning (GL), as a decentralized alternative to federated learning (FL), is more suitable for resource-constrained wireless networks, such as Flying Ad-Hoc Networks (FANETs) that are formed by unmanned aerial vehicles (UAVs). GL can significantly enhance the efficiency and extend the battery life of UAV networks.

In this paper, we propose a distributed GAN training method based on Gossip Learning, which realizes decentralized communication and collaborative learning among clients. We also use the Meta-Learning framework to improve the model's generalization ability in few-shot settings.

The emerging machine learning paradigm of decentralized federated learning (DFL) has the promise of greatly boosting the deployment of artificial intelligence (AI) by directly learning across distributed agents without centralized coordination.