Network Intrusion Detection Systems (NIDS) based on deep learning often fail to detect attack classes that are absent from training data, limiting their robustness against zero-day threats. This property called transferability across attack classes, remains underexplored especially in federated learning (FL) settings where data heterogeneity and client drift pose additional challenges. Prior work (TabFIDS) showed that FedAvg combined with bootstrapping and temporal averaging improves transferability for cross-device NIDS. In this paper, we explore whether other federated optimizers can do better. We compare two optimizers inspired by FedAdam and FedProx against the FedAvg baseline on the CIC-IDS 2017 dataset using an 11-client federated setup, sweep 10 hyperparameter configurations, and contrast two data-balancing strategies: bootstrapping and SMOTE. Using the count of client pairs whose transferred-model accuracy exceeds 70% as our primary metric (following TabFIDS), the proximal optimizer with a strong regularization term yields 48 transferable pairs versus 42 for FedAvg, while the adaptive optimizer with an aggressive server learning rate yields 46 pairs. SMOTE improves per-pair accuracy over bootstrapping but reduces breadth, revealing a depth-vs-breadth trade-off: high accuracy on a few client pairs versus moderate accuracy across most. These results indicate that the choice of federated optimizer, the rule by which client updates are regularized and aggregated into the global model, is very important for federated NIDS.

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