{"type":"ScholarlyArticle","title":"Robust Federated Hyperspectral Image Clustering","authors":[{"name":"Xiang Yang","slug":"xiang-yang","given":"Xiang","family":"Yang","affiliation":""},{"name":"Zhengzhong Zhu","slug":"zhengzhong-zhu","given":"Zhengzhong","family":"Zhu","affiliation":""},{"name":"Dayu Hu","slug":"dayu-hu","given":"Dayu","family":"Hu","affiliation":""},{"name":"Xiaowen Ma","slug":"xiaowen-ma","given":"Xiaowen","family":"Ma","affiliation":""},{"name":"Zihao Li","slug":"zihao-li","given":"Zihao","family":"Li","affiliation":""},{"name":"Wenxuan Tu","slug":"wenxuan-tu","given":"Wenxuan","family":"Tu","affiliation":""},{"name":"Taichun Zhou","slug":"taichun-zhou","given":"Taichun","family":"Zhou","affiliation":""},{"name":"Wenxin Zhang","slug":"wenxin-zhang","given":"Wenxin","family":"Zhang","affiliation":""},{"name":"Renxiang Guan","slug":"renxiang-guan","given":"Renxiang","family":"Guan","affiliation":""}],"abstract":"Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physical distribution of data across isolated silos. While Federated Learning offers a decentralized solution, existing frameworks in remote sensing are predominantly confined to supervised paradigms and struggle to address the heavy reliance on annotations and Non-IID distributions inherent among clients. To overcome these limitations, we propose a novel framework named Robust Federated HSI Clustering(RFHC) that enables collaborative unsupervised learning without requiring raw data exchange. Specifically, we design a dual-encoder architecture that incorporates a Federated Model Weight Augmentation strategy(FMWA), which generates consistent views through local-global network interactions to mitigate the spectral distortion introduced by traditional data augmentation. Furthermore, we develop a hybrid optimization objective that synergizes prototype relationship optimization with contrastive learning. This mechanism utilizes global prototypes to guide local training, effectively stabilizing feature learning against data heterogeneity while ensuring intra-cluster compactness. Extensive experiments on three benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed method against state-of-the-art model(SOTA) federated approaches.","doi":"10.24963/ijcai.2026/579","url":"https://ijcai.org/proceedings/2026/579","pdf":"https://ijcai.org/proceedings/2026/0579.pdf","markdown":"https://ijcai.org/proceedings/2026/579.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/579","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":579,"pages":{"first":5200,"last":5208},"keywords":["Machine Learning: Clustering"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}