My federated learning project just showed that "high accuracy" can completely hide a model missing every single attack from an entire category, and I think more people should know about this [R]

So for context, I've been working on this research project comparing federated learning algorithms (FedAvg, FedProx, FedNova) against a centralized baseline for network intrusion detection, using the CICIDS2017 dataset split across four simulated "silos" by attack type. Three of the silos have tons of data, but one silo (Web Attacks) only has about 3k samples out of 3 million total, so it's a pretty extreme imbalance. The thing that got me was h