Objective: This study provides a comprehensive threat analysis of data poisoning vulnerabilities across major health care AI architectures. The goals are to (1) identify attack surfaces in clinical AI ...
Most widely cited AI coding benchmarks, including the original SWE-bench, were built primarily around Python repositories, meaning headline performance results may not accurately predict how coding ag ...
Building upon the foundations laid by the Towards the European Health Data Space (TEHDAS) joint action (JA) and the new European Interoperability Framework (EIF), the toolkit incorporates several key ...
Abstract: Data trading significantly enhances data-driven technologies by enabling efficient data sharing across mobile devices and communication systems. Despite the clear advantages of incorporating ...
Abstract: Federated Learning (FL) has emerged as a promising framework to address data privacy concerns associated with mobile devices, in contrast to conventional Machine Learning (ML). However, ...
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