Federated Learning in ClimAIr: A Privacy-Preserving Approach to Data Integration & Analysis in Health & Environmental Research

Unlike traditional machine learning approaches that require the transfer of raw data to a central location, federated learning allows data to remain on local servers—typically within hospitals or research institutions—while only model updates (such as weights or gradients) are exchanged with a central coordinating server. This approach ensures that personal data never leaves its original source, significantly enhancing privacy, data security, and regulatory compliance.

Within the ClimAIr project, federated learning plays a foundational role in the analysis of health data related to respiratory conditions such as asthma and allergic rhinitis. Data is collected across nine clinical sites in Europe, encompassing diverse environmental and demographic variables. Federated learning enables the project to conduct AI-based research across these decentralised datasets while adhering to the principles of the General Data Protection Regulation (GDPR). This is crucial in health research, where patient confidentiality and ethical standards are paramount.

Key Applications of Federated Learning in ClimAIr:

• Privacy-Preserving Data Analysis: All patient data remains securely within the clinical institutions where it was collected. This eliminates the risks associated with data centralisation and ensures compliance with data protection laws.
• Collaborative Model Training: Instead of pooling data, ClimAIr aggregates encrypted model updates from each site. This distributed learning process creates robust predictive models that reflect variations in pollution exposure, climate conditions, and patient health profiles across Europe.
• Data-Driven Insights into Environmental Health: Federated learning supports the creation of AI models that identify links between climate factors, air pollution, and respiratory health outcomes. These insights help predict disease patterns and inform public health interventions.
• Scalability and Computational Efficiency: By avoiding large-scale data transfers, federated learning reduces network load and operational costs while maintaining high computational performance. It allows seamless integration of new clinical sites without reconfiguring the entire system.
• Transparency and Trust: The ClimAIr project also integrates Explainable Artificial Intelligence (XAI) techniques, ensuring that model predictions are interpretable and traceable. This promotes stakeholder trust and supports clinical decision-making.

Advancing Ethical AI in Health and Climate Research
Federated learning in ClimAIr sets a benchmark for responsible AI deployment in multidisciplinary research. It enables high-quality, generalisable models without compromising individual privacy or institutional data sovereignty. Moreover, by fostering collaboration across borders, federated learning enhances the reliability and applicability of AI-driven health assessments under changing environmental conditions.