Acute ischemic stroke (AIS) is a major cause of mortality and long-term disability globally. It requires rapid and accurate diagnosis and timely treatment to reduce irreversible brain damage and improve patient outcomes. In recent years, artificial intelligence (AI) has shown promising support in AIS diagnosis and management, particularly through analysis of medical imaging and clinical data. However, the practical deployment of AI in real-world stroke care remains constrained by data privacy regulations, institutional data silos, uneven distribution of medical resources, and concerns regarding model bias, interpretability, and trustworthiness. Federated learning (FL) has emerged as a pivotal technology supporting collaborative artificial intelligence (CAI), which offers a decentralised and privacy-preserving framework that enables many healthcare institutions to develop robust AI models without sharing raw patient data.
The primary aim of this study is to summarize how FL can address unique clinical, technical, and ethical challenges of AIS and highlight key advantages and implementation strategies, and assess its potential to transform multi-institutional collaboration in stroke diagnosis and treatment. A central advantage of FL is its ability to reconcile the need for large and diverse datasets with strict data protection requirements enforced by regulations like GDPR, CCPA, and APPI.
FL facilitates compliant cross-institutional collaboration while significantly reducing privacy risks linked with centralised data aggregation by keeping sensitive imaging and clinical data local to each institution and sharing only encrypted model updates. This is particularly important in AIS, where imaging data like CT, MRI, angiography, and perfusion scans are both highly informative and highly sensitive. FL supports the integration of complex, multimodal data by aggregating learned knowledge from heterogeneous sources, resulting in models with improved generalisation in diverse patient populations and clinical settings. FL-based systems offer practical benefits through efficient inference and adaptability, leverage techniques like federated domain adaptation, domain generalisation, and incremental learning to maintain performance under changing clinical conditions.
Another critical contribution of FL to CAI is improving fairness and reducing bias in AI-driven decision support. Training models in many institutions exposes them to broader demographic and geographic variability, helping to mitigate performance disparities in age, sex, ethnicity, and comorbidity subgroups. Recent fairness-aware FL methods, including adaptive aggregation and subgroup-sensitive optimisation, suggest that collaborative learning can significantly decrease variability in predictive performance and promote more equitable clinical outcomes. Trust and clinical acceptance are further strengthened by advances in explainable FL, which improve transparency by analysing model behaviour, incorporating expert knowledge, and supporting human-centred decision-making.
Platforms like FedEYE, originally designed for ophthalmic diseases, illustrate how domain-adapted FL infrastructure can support medical imaging workflows, secure model management, and user-friendly deployment. Features like low-code interfaces, modular data and algorithm pools, and support for edge–cloud collaboration make such platforms accessible to both academic centres and resource-limited hospitals. This flexibility is crucial because institutions face distinct constraints: large academic centres may contribute extensive datasets and advanced expertise, and local hospitals benefit from access to high-quality models without substantial computational investment. Real-world and experimental evidence increasingly support the feasibility of FL in AIS, with multi-centre studies reporting robust performance in infarct segmentation, biomarker estimation, risk prediction, EEG-based severity assessment, and real-time screening. FL-based approaches are approaching the performance of centralised learning while maintaining superior cross-site robustness.
The deployment of FL-based CAI systems must carefully address potential threats, including inference attacks, data leakage, and model poisoning risks. These risks can be mitigated through established privacy-enhancing technologies such as secure aggregation, differential privacy, homomorphic encryption, and multi-party computation, ensuring both regulatory compliance and patient trust. Looking forward, future research should prioritise optimising FL systems for emergency response, establishing standardised guidelines for governance and evaluation, and enabling personalised, institution-specific model configurations. The integration of generative models, including large language models and diffusion models, within FL frameworks also offers promising avenues for privacy-preserving data augmentation, improved interpretability, and reduced data imbalance.
FL represents a transformative enabler of collaborative AI for AIS, effectively bridging the gap between data privacy, fairness, scalability, and clinical utility. FL has the potential to accelerate the translation of AI innovations into trustworthy clinical tools and improve diagnostic accuracy, treatment decision-making, and patient outcomes in AIS care by fostering secure, equitable, and efficient cross-institutional collaboration.
Reference: Fan Z, Chen Q, Lu W, et al. Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke. Ann Med. 2026;58(1). doi:10.1080/07853890.2025.2594356
