Federated Learning’s Privacy Puzzle: Measuring and Blocking Data Leakage

New research introduces a robust method for quantifying the risk of data reconstruction attacks in federated learning, paving the way for stronger privacy guarantees.

New research introduces a robust method for quantifying the risk of data reconstruction attacks in federated learning, paving the way for stronger privacy guarantees.
A new study demonstrates how graph neural networks can effectively model concrete composition and predict compressive strength, rivaling established machine learning techniques.

New research reveals a modular, expert-based approach to sepsis prediction surpasses complex neural networks, particularly in environments with limited data.

A new study assesses how reliably machine learning algorithms can pinpoint and diagnose electrical faults in power systems under real-world data limitations.

A new analysis of social media data reveals the emotional landscape of Bangladesh’s recent mass uprising, offering insights into public sentiment during a period of intense political and social change.

New research reveals that a healthy degree of variation among artificial intelligence models is key to preventing performance degradation and maintaining robust knowledge over time.

New research suggests that how AI technology is released-openly or behind closed doors-has a measurable impact on financial markets.

New research reveals that streamlined large language models can outperform their massive counterparts in complex financial analysis.
A new framework, SeBERTis, leverages the power of deep learning to understand the meaning behind security issue reports, improving accuracy and reducing reliance on simple text matching.

A new framework leverages artificial intelligence to provide tailored support and guidance to farmers facing the challenges of a changing climate.