10 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
Elaheh Jafarigol, Theodore Trafalis, Talayeh Razzaghi +1
In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning la…
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
Elaheh Jafarigol, Theodore Trafalis
In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the priv…
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs
Elaheh Jafarigol, Theodore Trafalis
The classification of weather data involves categorizing meteorological phenomena into classes, thereby facilitating nuanced analyses and precise predictions for various sectors su…
A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
Elaheh Jafarigol, Theodore Trafalis, Neshat Mohammadi
For over two decades, detecting rare events has been a challenging task among researchers in the data mining and machine learning domain. Real-life problems inspire researchers to…