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
Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be present in the code produced by LLMs. To solve this issue, we propose a novel fairness framework, i.e., Solar, to assess and…
Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for…