Adaptive decentralized federated learning in wireless mesh network

The overall goal of the FLESHNET project is to deliver building blocks for adaptive decentralized federated learning experimentally validated in the realistic conditions of a wireless mesh network testbed. Both the EU and US partners will participate in the analysis of the experimental results. The design will be complemented by the US partners with the networking perspective, while the EU partner will complement the design with an adaptive and decentralized model.





Additional Info

Federated learning has recently appeared as a promising machine learning approach which respects the privacy of the data. We envision novel federated learning applications in which any node can participate in the provision of data and in the training of machine learning models. For this to happen, federated learning building blocks must be extended and evaluated. These building blocks will enable the autonomy of the participants for taking self-determined decisions, facilitate the ownership of machine learning models and data, and enable decentralized governance of the data. This scenario will be realistic in the near future as edge nodes of a federated learning network will receive an ever increasing amount of data coming from more and more nearby sensors. The experiment obtains feedback from a prototype, experimentally evaluated in a testbed deployed within the GuifiSants wireless city mesh network.

Enduser Relevance

By using experimentation within a wireless mesh network testbed, our research aims to understand the effects of federated learning designs and models when faced with real device deployments.


Felix Freitag, (Universitat Politecnica de Catalunya)

Not available yet

This experiment is currently underway.

Country:  Spain United States

Keyword: DDGPETservices

Status: Early research demo

Category: Measurement, monitoring, analysis and abuse handling

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