10 citations · 10 across the 4 of their papers we have counts for
4 papers
BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge
Guanqiao Qu, Shuo Chen, Qian Chen +2
Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: A…
Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observability
Paul J. Pritz, Kin K. Leung
Reinforcement learning in partially observable environments is typically challenging, as it requires agents to learn an estimate of the underlying system state. These challenges ar…
Ultra-Low-Latency Edge Intelligent Sensing: A Source-Channel Tradeoff and Its Application to Coding Rate Adaptation
Qunsong Zeng, Jianhao Huang, Zhanwei Wang +2
The forthcoming sixth-generation (6G) mobile network is set to merge edge artificial intelligence (AI) and integrated sensing and communication (ISAC) extensively, giving rise to t…
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
Zheng Lin, Guanqiao Qu, Wei Wei +2
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated lear…