  As the development of deep learning techniques in autonomous landing systems
continues to grow, one of the major challenges is trust and security in the
face of possible adversarial attacks. In this paper, we propose a federated
adversarial learning-based framework to detect landing runways using paired
data comprising of clean local data and its adversarial version. Firstly, the
local model is pre-trained on a large-scale lane detection dataset. Then,
instead of exploiting large instance-adaptive models, we resort to a
parameter-efficient fine-tuning method known as scale and shift deep features
(SSF), upon the pre-trained model. Secondly, in each SSF layer, distributions
of clean local data and its adversarial version are disentangled for accurate
statistics estimation. To the best of our knowledge, this marks the first
instance of federated learning work that address the adversarial sample problem
in landing runway detection. Our experimental evaluations over both synthesis
and real images of Landing Approach Runway Detection (LARD) dataset
consistently demonstrate good performance of the proposed federated adversarial
learning and robust to adversarial attacks.
