Intrusion Detection Systems (IDS) play a crucial role in safeguarding systems and networks from different types of attacks. However, IDSes face significant hurdles in detecting Advanced Persistent Threats (APTs), which are sophisticated cyber-attacks characterised by their stealth, duration, and advanced techniques. Recent research has explored the effectiveness of Graph Neural Networks (GNNs) in APT detection, leveraging their ability to analyse intricate-relationships within graph data. However, existing approaches often rely on local models, limiting their adaptability to evolving APT-tactics and raising privacy-concerns. In response to these challenges, this paper proposes integrating Federated-Learning (FL) into the architectures of GNN-based Intrusion Detection Systems. Moreover, our solution includes an enhanced encryption-system of the clients’ weights to safely send them to the server through the system’s network. This solution prevents man-in-the-middle (MitM) attacks from intercepting the weights and reconstructing clients data using reverse engineering. We evaluate our approach on several datasets, demonstrating promising results in reducing false-positive rates compared to state-of-the-art Provenance-based IDSes (PIDS).