Federated Adversarial Learning for Robust Autonomous Landing Runway Detection

• URL: http://arxiv.org/abs/2406.15925v1
• Date: Sat, 22 Jun 2024 19:31:52 GMT
• Title: Federated Adversarial Learning for Robust Autonomous Landing Runway Detection
• Authors: Yi Li, Plamen Angelov, Zhengxin Yu, Alvaro Lopez Pellicer, Neeraj Suri,
• Abstract summary: In this paper, we propose a federated adversarial learning-based framework to detect landing runways. 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.
• Score: 6.029462194041386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Defending against Data Poisoning Attacks in Federated Learning via User Elimination [0.0]
  This paper introduces a novel framework focused on the strategic elimination of adversarial users within a federated model. We detect anomalies in the aggregation phase of the Federated Algorithm, by integrating metadata gathered by the local training instances with Differential Privacy techniques. Our experiments demonstrate the efficacy of our methods, significantly mitigating the risk of data poisoning while maintaining user privacy and model performance.
  arXiv  Detail & Related papers  (2024-04-19T10:36:00Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Certified Robustness in Federated Learning [54.03574895808258]
  We study the interplay between federated training, personalization, and certified robustness. We find that the simple federated averaging technique is effective in building not only more accurate, but also more certifiably-robust models.
  arXiv  Detail & Related papers  (2022-06-06T12:10:53Z)
• Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection [24.562359531692504]
  We propose DifFense, an automated defense framework to protect an FL system from backdoor attacks. Our detection method reduces the average backdoor accuracy of the global model to below 4% and achieves a false negative rate of zero.
  arXiv  Detail & Related papers  (2022-02-21T17:13:03Z)
• Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis [96.53859361560505]
  Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. There always exists severe domain shift between the pretraining and downstream ABSA datasets. We introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline.
  arXiv  Detail & Related papers  (2021-10-26T04:03:45Z)
• Improving Model Robustness with Latent Distribution Locally and Globally [28.99007833855102]
  In this work, we consider model robustness of deep neural networks against adversarial attacks from a global manifold perspective. We propose a novel adversarial training method through robust optimization, and a tractable way to generate Latent Manifold Adrial Examples (LMAEs) The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-07-08T07:52:53Z)
• Privacy-Preserving Federated Learning on Partitioned Attributes [6.661716208346423]
  Federated learning empowers collaborative training without exposing local data or models. We introduce an adversarial learning based procedure which tunes a local model to release privacy-preserving intermediate representations. To alleviate the accuracy decline, we propose a defense method based on the forward-backward splitting algorithm.
  arXiv  Detail & Related papers  (2021-04-29T14:49:14Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
  In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
  arXiv  Detail & Related papers  (2020-08-13T04:26:31Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.