Federated Learning Framework for Privacy-Preserving Clinical Named Entity Recognition

Abstract

Federated learning has emerged as a promising paradigm for collaborative model training without centralized data aggregation. This approach offers a privacy-preserving framework capable of accommodating stringent requirements associated with medical data. Clinical Named Entity Recognition relies on identifying and extracting pertinent medical concepts from unstructured text. However, the sharing of sensitive clinical information raises data ownership and privacy concerns, hindering collaborative progress. Leveraging federated learning circumvents these challenges by enabling multiple clinical sites to train shared models without exchanging patient data. This paper examines an advanced federated learning framework designed to address the privacy constraints of clinical text, focusing on sophisticated embedding techniques for named entities as well as specialized aggregation protocols to ensure secure model updates. Beyond classical encryption, the proposed approach includes theoretical and practical considerations that balance performance and confidentiality. Through the integration of encryption schemes and noise perturbations, the architecture supports real-time collaboration among institutions to broaden the scope and scale of data-driven medical research. Extensive theoretical analysis and experimentation demonstrate the feasibility of privacy-preserving implementations for tasks that require domain-specific accuracy. This work offers robust insights, including how encryption, aggregation, and distributed machine learning can be unified to tackle the unique challenges of clinical named entity recognition, thereby facilitating both improved patient outcomes and research discoveries.