[P] Federated Adversarial Learning

I'm a CS/ML engineering student in my 4th year, and I need help for a project I recently got assigned to (as an "end of the year" project).

I am familiar with basic ML stuff, deep learning etc and made a few "standard" projects here and there about it... However I found this topic a bit challenging since it combines both FL and the adversarial aspect, I did a lot of research especially on arxiv to try to understand the gist of it.

HOWEVER, the subject is essentially "federated adversarial learning" and I am struggeling to understand what I'm supposed to do. (I found ONE article on arxiv but ngl i find it very hard to understand as it is very theoritical.)

I talked to my teachers/supervisors about this but they said "do whatever you want" which doesn't help AT ALL..... They did provide a dataset which is CICIDS2017 which I thought of using since it's already seprated in multiple csv files (as the clients).

My problem now is that I can possibly do the federated learning part using frameworks like Flower, however I am quite confused as to how I could make "federated adversarial learning". I've only found articles abt adversarial training by using adversarial examples but in the context of CICIDS2017 (or any similar dataset) idk how i'm supposed to do that. I did understand it in images, by modifying certain pixels (using FGSM for ex) we can trick a model into misclassifcation. But am I supposedd to make an algorithm to generate those examples ? Or should I think of another project that invovles adversarial "learning" ? I am not sure because this subject is very vague to me, and also I don't understand the difference between adv training and learning. Nothing is really "concrete" if it makes any sense.
If anyone has a more specfic idea of this subject, it would be very helpful.

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