Conveners
QSR-Informs Invited Session
- Mostafa Reisi Gahrooei (University of Florida)
We propose a novel federated learning framework on a network of clients with heterogeneous data. Unlike conventional federated learning, which creates a single aggregated model shared across all nodes, our approach develops a personalized aggregated model for each node using the information (and not the raw data) of neighboring nodes in the network. To do so, we leverage the topology of the...
In the realm of machine learning, effective modeling of heterogeneous subpopulations presents a significant challenge due to variations in individual characteristics and behaviors. This paper proposes a novel approach for addressing this issue through Multi-Task Learning (MTL) and low-rank decomposition techniques. Our MTL approach aims to enhance personalized modeling by leveraging shared...
Diffusion models have proven effective for generative modeling, denoising, and anomaly detection due to their ability to capture complex, high-dimensional, and non-linear data distributions. However, they typically require large amounts of non-anomalous training data, limiting their use when data is unlabeled and contains a mix of contaminated and uncontaminated samples. We propose a novel...