  Fast identification of new network attack patterns is crucial for improving
network security. Nevertheless, identifying an ongoing attack in a
heterogeneous network is a non-trivial task. Federated learning emerges as a
solution to collaborative training for an Intrusion Detection System (IDS). The
federated learning-based IDS trains a global model using local machine learning
models provided by federated participants without sharing local data. However,
optimization challenges are intrinsic to federated learning. This paper
proposes the Federated Simulated Annealing (FedSA) metaheuristic to select the
hyperparameters and a subset of participants for each aggregation round in
federated learning. FedSA optimizes hyperparameters linked to the global model
convergence. The proposal reduces aggregation rounds and speeds up convergence.
Thus, FedSA accelerates learning extraction from local models, requiring fewer
IDS updates. The proposal assessment shows that the FedSA global model
converges in less than ten communication rounds. The proposal requires up to
50% fewer aggregation rounds to achieve approximately 97% accuracy in attack
detection than the conventional aggregation approach.
