  Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks
(FANETs) encounter security challenges due to the dynamic and distributed
nature of these networks. Previous studies predominantly focused on centralized
intrusion detection, assuming a central entity responsible for storing and
analyzing data from all devices.However, these approaches face challenges
including computation and storage costs, along with a single point of failure
risk, threatening data privacy and availability. The widespread dispersion of
data across interconnected devices underscores the necessity for decentralized
approaches. This paper introduces the Federated Learning-based Intrusion
Detection System (FL-IDS), addressing challenges encountered by centralized
systems in FANETs. FL-IDS reduces computation and storage costs for both
clients and the central server, crucial for resource-constrained UAVs.
Operating in a decentralized manner, FL-IDS enables UAVs to collaboratively
train a global intrusion detection model without sharing raw data, thus
avoiding the delay in decisions based on collected data, as is often the case
with traditional methods. Experimental results demonstrate FL-IDS's competitive
performance with Central IDS (C-IDS) while mitigating privacy concerns, with
the Bias Towards Specific Clients (BTSC) method further enhancing FL-IDS
performance even at lower attacker ratios. Comparative analysis with
traditional intrusion detection methods, including Local IDS (L-IDS), sheds
light on FL-IDS's strengths. This study significantly contributes to UAV
security by introducing a privacy-aware, decentralized intrusion detection
approach tailored to UAV networks. Moreover, by introducing a realistic dataset
for FANETs and federated learning, our approach differs from others lacking
high dynamism and 3D node movements or accurate federated data federations.
