FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models
J Vora, N Bouacida, A Krishnan… - arxiv preprint arxiv …, 2024 - arxiv.org
We introduce FedDM, a novel training framework designed for the federated training of
diffusion models. Our theoretical analysis establishes the convergence of diffusion models …
diffusion models. Our theoretical analysis establishes the convergence of diffusion models …
A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems
We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase
optimal power flow in active distribution systems with dynamically changing topologies. To …
optimal power flow in active distribution systems with dynamically changing topologies. To …
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Error-bounded lossy compression has been effective in significantly reducing the data
storage/transfer burden while preserving the reconstructed data fidelity very well. Many error …
storage/transfer burden while preserving the reconstructed data fidelity very well. Many error …
Efficient cross-silo federated learning using a computing power-aware scheduler
Z Li - 2024 - ideals.illinois.edu
Cross-silo federated learning offers a promising solution to collaboratively train robust and
generalized machine learning models in domains such as healthcare, finance, and scientific …
generalized machine learning models in domains such as healthcare, finance, and scientific …