9 citations · 9 across the 4 of their papers we have counts for
4 papers
Multi-Turn Distributed Inference with Mixture of Experts for 6G Edge--Cloud Networks
Bo Liu, Haiyuan Li, Yuelin Liu +4
Mixture-of-Experts (MoE) architectures are increasingly deployed across 6G edge--cloud networks, where sparse activation reduces the computational footprint of each inference to on…
Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
Yuelin Liu, Haiyuan Li, Xenofon Vasilakos +2
Future networks (including 6G) are poised to accelerate the realisation of Internet of Everything. However, it will result in a high demand for computing resources to support new s…
Lifecycle Management of Trustworthy AI Models in 6G Networks: The REASON Approach
Juan Parra-Ullauri, Xueqing Zhou, Shadi Moazzeni +11
Artificial Intelligence (AI) is expected to play a key role in 6G networks including optimising system management, operation, and evolution. This requires systematic lifecycle mana…
Federated Fairness Analytics: Quantifying Fairness in Federated Learning
Oscar Dilley, Juan Marcelo Parra-Ullauri, Rasheed Hussain +1
Federated Learning (FL) is a privacy-enhancing technology for distributed ML. By training models locally and aggregating updates - a federation learns together, while bypassing cen…