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Found 5 papers, 0 papers with code
ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours
In this work, we conducted a comprehensive analysis on the AlphaFold training procedure based on Openfold, identified that inefficient communications and overhead-dominated computations were the key factors that prevented the AlphaFold training from effective scaling.
Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG
At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO have been key enablers across successive generations of cellular networks with evolving complexity and design challenges.
Distributed Learning Meets 6G: A Communication and Computing Perspective
With the ever-improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest towards exploring Distributed Learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks.
Federated Dynamic Spectrum Access
In this article, we introduce a Federated Learning (FL) based framework for the task of DSA, where FL is a distributive machine learning framework that can reserve the privacy of network terminals under heterogeneous data distributions.
Differential Privacy Meets Federated Learning under Communication Constraints
The performance of federated learning systems is bottlenecked by communication costs and training variance.
