ML-TN-007 — AI at the edge: exploring Federated Learning solutions

Info Box Applies to Machine Learning Applies to Machine Learning Technical Notes

History

Version	Date	Notes
1.0.0	July 2023	First public release
17342	17/01/2023	Update testbed information

Introduction

According to Wikipedia, Federated Learning (FL) is defined as a machine learning technique that trains an algorithm via multiple independent sessions, each using its own dataset. This approach stands in contrast to traditional centralized machine learning techniques where local datasets are merged into one training session, as well as to approaches that assume that local data samples are identically distributed.

Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights and access to heterogeneous data. Its applications engage industries including defense, telecommunications, Internet of Things, and pharmaceuticals. A major open question is when/whether federated learning is preferable to pooled data learning. Another open question concerns the trustworthiness of the devices and the impact of malicious actors on the learned model.

In principle, FL can be an extremely useful technique to address critical issues of industrial IoT applications. Therefore, it perfectly matches DAVE Embedded Systems' IIoT platform, ToloMEO. This Technical Note (TN) illustrates several tests DAVE Embedded Systems run on different embedded platforms for exploring and characterizing some of the most promising open-source FL frameworks available, which could boost our ToloMEO-compliant products in the future.