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→Introduction
''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. ThereforeAs such, it perfectly matches [[ToloMEO Embedded Assistant|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 to date. One of these frameworks might equip ToloMEO-compliant products in the future allowing our customers to industrial federated learning systems easily.From the point of view of machine learning, therefore, we investigated if the embedded architectures available today are suited for acting not only as inference platforms — we arleady dealt with this issue [[ML-TN-001 - AI at the edge: comparison of different embedded platforms - Part 1|here]] — but as training platforms as well. == Federated learning frameworks ==When we chose which frameworks to test, we set some requirements:* open-source* permissive license