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 \(t_k\) ).

May 16, 2012 · 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.

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 …

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.

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 …

The result of our comparison is that gossip learning is in general comparable to the centrally coordinated federated learning approach, and in many scenarios gossip learning actually outperforms federated learning.

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...

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.