Our goal is to provide a federated learning framework that is both secure, scalable and easy-to-use. We believe that that minimal code change should be needed to progress from early proof-of-concepts ...

This paper designs a federated learning structure with a two-layer game for vehicular ... vehicle and computing resource allocation as well as cache updates through a hierarchical distributed approach ...

Over-Encoding (OE) scales input vocabularies exponentially using hierarchical n-gram embeddings ... suggesting richer input representations accelerate learning. Sparse Parameter Efficiency: Despite ...

Traditional federated learning approaches fail to account for the unique characteristics of local vehicle data, limiting the model performances. To address this, we propose a personalized hierarchical ...

For over a century, organizations have relied on hierarchy. It's comfortable, predictable, and, for some, deeply ingrained. Decisions flow downward, power stays at the top, and employees are ...

Dr Amanda Cole, a lecturer at the University of Essex, says there is a "hierarchy of accents" in the UK, with accents from industrialised urban areas like Glasgow and Birmingham often seen as low ...

Abstract: Prompt learning has become a prevalent strategy for adapting ... Consequently, we propose a novel approach called Hierarchical Prompt Tuning (HPT), which enables simultaneous modeling of ...

That all changed when they adopted federated learning. Federated learning is emerging as a game-changing approach for enterprises looking to leverage the power of LLMs while maintaining data ...

This constraint limits LLMs in performing tasks that require reasoning across hierarchical information ... He is passionate about data science and machine learning, bringing a strong academic ...