@inproceedings{paul-etal-2025-fedclimask,
    title = "{F}ed{C}li{M}ask: Context-Aware Federated Learning with Ontology-Guided Semantic Masking for Clinical {NLP}",
    author = "Paul, Srijit  and
      Das, Sajeeb  and
      Muhury, Ucchas  and
      Islam, Akib Jayed  and
      Barua, Dhruba Jyoti  and
      Salehin, Sultanus  and
      Datta, Prasun",
    editor = "Das, Sudhansu Bala  and
      Mishra, Pruthwik  and
      Singh, Alok  and
      Muhammad, Shamsuddeen Hassan  and
      Ekbal, Asif  and
      Das, Uday Kumar",
    booktitle = "Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, BULGARIA",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.globalnlp-1.19/",
    pages = "172--180",
    abstract = "Clinical federated learning faces critical challenges from statistical heterogeneity across healthcare institutions and privacy requirements for sensitive medical data. This work implements the foundational components of FedCliMask and proposes a comprehensive framework for privacy-preserving federated learning in clinical settings that combines ontology-guided semantic masking with context-aware federated aggregation. Our framework addresses the dual challenges of privacy preservation and statistical heterogeneity through two key innovations: (1) ontology-guided semantic masking using UMLS hierarchies to provide graduated privacy protection while preserving clinical semantics, and (2) context-aware federated aggregation that considers hospital-specific features including medical specialties, data complexity, privacy levels, and data volume. The semantic masking component is implemented and evaluated on synthetic clinical data, demonstrating effective privacy-utility tradeoffs across four masking levels. The context-aware analysis component is also implemented successfully profiling 12,996 synthetic clinical notes across 6 diverse hospitals to demonstrate meaningful hospital differentiation. The complete framework is designed to enable privacy-preserving clinical trial recruitment through federated learning while adapting to institutional heterogeneity."
}