ASR-FED: Agnostic Straggler Resilient Federated Algorithm for Drone Networks Security

Federated Learning (FL) enables knowledge sharing among distributed edge devices while ensuring data privacy. However, implementing the FL technique in dynamic networks like the drone network, is challenged by the high communication cost among the clients and slow convergence rate of the global model due to the straggling clients in the network. Moreover, the straggler effect can exacerbate attack propagation in a security network, as attackers might exploit the delay of the intrusion detection model designed in the presence of stragglers. The semi-asynchronous FL (SAFL) method has displayed commendable performance in mitigating the straggler effect. However, existing SAFL techniques do not consider a holistic approach that improves the performance of the global model at a reduced communication cost. This study proposes an agnostic straggler-resilient SAFL (ASR-Fed) algorithm that prioritizes the updates of high-performing and efficient clients while circumventing the updates of straggling clients during the FL process. Simulation experiments performed under different scenarios evaluate the effectiveness of ASR-Fed. The results validate the robustness of ASR-Fed in enhancing the detection performance of the cybersecurity model achieving an accuracy above 98.5 % within the least communication round. Outperforming existing state-of-the-art FL aggregating protocols.