1 citations · 3 across the 4 of their papers we have counts for
4 papers
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
Nathaniel Hudson, Valerie Hayot-Sasson, Yadu Babuji +5
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks…
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…
Accelerating Communications in Federated Applications with Transparent Object Proxies
J. Gregory Pauloski, Valerie Hayot-Sasson, Logan Ward +5
Advances in networks, accelerators, and cloud services encourage programmers to reconsider where to compute -- such as when fast networks make it cost-effective to compute on remot…
Hierarchical and Decentralised Federated Learning
Omer Rana, Theodoros Spyridopoulos, Nathaniel Hudson +4
Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, th…