How to Test for AI Fairness | HackerNoonThe research focuses on developing fair supervised learning models using different datasets to evaluate performance towards fairness in predictions.
Algorithm Protection in the Context of Federated LearningThe article focuses on securing ML algorithms in healthcare through federated learning and robust protection against intellectual property theft.
The Trials and Triumphs of DPFL Research | HackerNoonThis paper reviews and evaluates the capabilities and limitations of Distributed Privacy-preserving Federated Learning (DPFL) methods, bridging theory with practical applications.
Why Some AI Power Flow Models Are Faster Than Others | HackerNoonPPFL methods are more computationally efficient compared to DPFL methods because they avoid training processes.
The Trials and Triumphs of DPFL Research | HackerNoonThis paper reviews and evaluates the capabilities and limitations of Distributed Privacy-preserving Federated Learning (DPFL) methods, bridging theory with practical applications.
Why Some AI Power Flow Models Are Faster Than Others | HackerNoonPPFL methods are more computationally efficient compared to DPFL methods because they avoid training processes.
Federated learning: The killer use case for generative AIFederated learning offers a more efficient, secure, and effective AI implementation strategy for enterprises.
NIST publishes guidance on privacy-preserving federated learning systemsThe U.S. National Institute of Standards and Technology released guidance on vertical privacy-preserving federated learning systems, emphasizing the balance between privacy risk and performance cost.
Enhancing Edge Computing with Federated Learning and AIDecentralized model training and federated learning reshape privacy and efficiency in edge AI.Integration of AI with 5G enhances capabilities of edge devices, enabling instantaneous processing and decision-making.