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Andrew Soltan

2 accepted papers

2026

Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling

AAAI 2026technical

Federated learning (FL) enables privacy-preserving model training across distributed Electronic Health Records (EHRs), but its deployment remains limited by data-view heterogeneity, where institutions maintain incompatible local schemas. Most existing methods address this by enforcing flat, aligned

Cited by 0SourcePDFScholar
2024

Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks

AISTATS 2024poster

The proliferation of decentralised electronic healthcare records (EHRs) across medical institutions requires innovative federated learning strategies for collaborative data analysis and global model training, prioritising data privacy. A prevalent issue during decentralised model training is the dat…

Cited by 9SourcePDFScholar