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''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 (IIoT) applications. As such, it matches [[ToloMEO Embedded Assistant|DAVE Embedded Systems' IIoT platform, ToloMEO]], perfectly. This Technical Note (TN) illustrates several tests how DAVE Embedded Systems run on different embedded platforms for exploring explored, tested, and characterizing characterized 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 implement federated learning systems easily. From the point of view of machine learning, therefore, we investigated if typical embedded architectures used today for industrial applications are suited for acting not only as inference platforms — we already 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.
In brief, the work consists of three main steps:* Selecting the FL frameworks to test.* Testing the selected frameworks.* Comparing the results for isolating the best framework.* Deepinvestigation of the best framework.A detailed dissertation of the work that led to this Technical Note is available here TBD. == Choosing Federated learning frameworks ==
When we chose which frameworks to test, we set some requirements:
* open-source
* permissive license
== Testing the selected frameworks ==
=== Flower ===
{| class="wikitable"
|+Flower running on SBC ORCA
!# of cores
!
=== NVFlare ===
[https://developer.nvidia.com/flare NVFlare] is
TBD
== Comparing test results ==
TBD
== Deep investigation of NVFlare ==
TBD
== Conclusions ==
TBD
labeling of new samples