3 accepted papers
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
This paper presents Adaptive Parameter Optimisation (APO), a novel framework for optimising shared models across multiple clinical tasks, addressing the challenges of balancing strict parameter sharing—often leading to task conflicts—and soft parameter sharing, which may limit effective cross-task i…
Federated Learning (FL) involves a server aggregating local models from clients to compute a global model. However, this process can struggle to position the global model in low-loss regions of the parameter space for all clients, resulting in subpar convergence and inequitable performance across cl…