The confusion this student is experiencing is not a sign of a poorly designed project; it is the natural starting point for meaningful work. Federated adversarial learning is a difficult topic because it sits at the intersection of two advanced fields, and the academic literature often assumes a level of fluency that most fourth-year students simply do not have yet. The student's instinct to break the problem into parts, federated learning with Flower and adversarial examples via FGSM, is exactly right. The missing piece is not technical skill but conceptual clarity about what "adversarial" means in this context. That clarity is achievable, and it starts with accepting that the project does not need to be novel to be valuable.
The student's real question is whether they should generate adversarial examples or do something else entirely. The answer is both, and neither is as complicated as it seems. In image classification, adversarial examples are created by adding small, calculated perturbations to pixels. In a tabular dataset like CICIDS2017, the same principle applies: you modify feature values slightly, guided by the gradient of the loss function, to push the model toward a wrong prediction. FGSM is a good starting point because it is simple to implement and understand. The student does not need to invent a new attack. They need to adapt an existing one to their data and then measure how the model's performance degrades. That is a concrete, defensible project on its own.
The harder question is what "federated adversarial learning" should mean in practice. The student has correctly noticed that most papers focus on adversarial training, which is a defense mechanism where the model is trained on adversarial examples to become more robust. That is not the same as adversarial learning in a federated setting, where the challenge is that one malicious client can poison the global model. The student could choose either direction, but the more interesting and tractable one is the latter: simulate a scenario where some clients send poisoned updates, then test whether the federated aggregation can resist it. This is a real research problem, it uses the provided dataset naturally, and it does not require the student to master every theoretical detail in the arxiv paper they found.
What this student needs is permission to simplify. They do not need to solve the problem the way a research lab would. They need to build a working system that demonstrates understanding of both components and shows how they interact. That means using Flower to simulate clients, training a baseline model, then introducing adversarial examples into the training data for a subset of clients and observing the impact on the global model. The project becomes a comparison: how does the model perform with and without adversarial clients? That is a clear, achievable goal. The teachers said "do whatever you want," which is unhelpful as guidance, but it is liberating as permission. The student should stop looking for the definitive interpretation and instead choose one that lets them learn, build, and explain. That is what the project is actually testing.