4 papers
Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks
Xiumei Deng, Zehui Xiong, Binbin Chen +3
Large language models (LLMs) are proliferating rapidly at the edge, delivering intelligent capabilities across diverse application scenarios. However, their practical deployment in…
Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission
Faranaksadat Solat, Joohyung Lee, Mohamed Seif +2
Hybrid Language Models (HLMs) combine the low-latency efficiency of Small Language Models (SLMs) on edge devices with the high accuracy of Large Language Models (LLMs) on centraliz…
Large Language Model (LLM)-enabled Graphs in Dynamic Networking
Geng Sun, Yixian Wang, Dusit Niyato +4
Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Mean…
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Xiumei Deng, Jun Li, Kang Wei +6
Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However,…