FedLD: Federated Learning for Privacy-Preserving Collaborative Landslide Detection

Landslide hazards pose a great threat to the local residents and infrastructure in mountain areas. Numerous technologies have been invented to monitor landslides, and large amounts of high-resolution spatio-temporal data are consistently emerging. These data are highly related to the national security. The local governments release legislation to regulate the sharing of these data. However, the existing landslide detection models explicitly or implicitly assume that landslide monitoring and mapping data are directly shared in a centralized server. There is a gap between landslide detection models and landslide data sharing. To bridge this gap, this letter proposes a privacy-preserving machine learning method named federated learning-based landslide detection (FedLD) for landslide detection. First, horizontal federated learning (HFL) is introduced to protect the data privacy of the modeling process of landslide detection, enabling the development of landslide detection models without direct sharing of the original landslide monitoring data. Second, a new marginal contribution (MC) metric is proposed to measure the contribution of the participants of federated landslide detection models and is used to develop a model aggregation algorithm for federated landslide detection. Experimental results demonstrated that FedLD is able to protect the privacy of popular deep learning-based landslide detection models and achieves competitive landslide classification performance. Therefore, federated learning (FL) provides an effective solution for promoting data sharing in landslide detection.