8 citations · 9 across the 6 of their papers we have counts for
4 papers · 1 filter
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
Sahil Tyagi, Andrei Cozma, Olivera Kotevska +1
Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework desi…
X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms
Yueming Yuan, Ahan Gupta, Jianping Li +3
Emerging expert-specialized Mixture-of-Experts (MoE) architectures, such as DeepSeek-MoE, deliver strong model quality through fine-grained expert segmentation and large top-k rout…
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models
Aristeidis Tsaris, Isaac Lyngaas, John Lagregren +6
Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images fr…
Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
Wesley Brewer, Aditya Kashi, Sajal Dash +4
In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligen…