HeteroRL is a novel heterogeneous reinforcement learning framework designed for stable and scalable training of large language models (LLMs) in geographically distributed, resource-heterogeneous ...
The digital landscape is evolving at an unprecedented pace. From smartphones and wearables to autonomous vehicles and hyperscale data centers, the demand for faster, smarter, and more efficient ...
The terms “chiplet” and “heterogeneous integration” fill news pages, conference papers, and marketing presentations, and for the most part engineers understand what they’re reading. But speakers ...
Abstract: Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication ...
1 School of Soil and Water Conservation, Beijing Forestry University, Beijing, China 2 National Disaster Reduction Center of China, Ministry of Emergency Management, Beijing, China Earthquake-induced ...
Researchers built a reliable breath collection and analysis method using thermo-desorption gas chromatography-mass spectrometry (TD-GC–MS) that can produce a comprehensive list of known volatile ...
Source code for the paper "Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks", this paper is pulished in ICC 2024.
Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper empirically investigates the relationship between uncertainty and trade. We use a gravity model for 143 ...
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