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Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data.
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information.
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs
Moreover, with advancements in federated learning, machine learning models can be trained across decentralized databases, ensuring privacy and data integrity while mitigating the need for centralized data storage and processing.
Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey
This study is an application of machine learning in identifying the underlying patterns in the data of 30 countries participating in the World Value Survey.
A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain.
