activity
20232026
most citedEchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control

10 citations · 11 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

TideKV: Delayed KV Eviction via Layer-Wise Chunked Prefill for Memory-Efficient Long-Context LLM Inference

Yu Wang, Zhu Wang, Ning Hu +4

Long-context large language model (LLM) inference is important for modern intelligent applications but remains constrained by peak memory during prompt prefill. Existing KV cache c…

cs.AI2024

AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices

Yuzhan Wang, Sicong Liu, Bin Guo +6

Deep learning is reshaping mobile applications, with a growing trend of deploying deep neural networks (DNNs) directly to mobile and embedded devices to address real-time performan…

cs.LG2024★ 1 cited

Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls

Sicong Liu, Wentao Zhou, Zimu Zhou +5

There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a v…

cs.DC2024

AdaOper: Energy-efficient and Responsive Concurrent DNN Inference on Mobile Devices

Zheng Lin, Bin Guo, Sicong Liu +4

Deep neural network (DNN) has driven extensive applications in mobile technology. However, for long-running mobile apps like voice assistants or video applications on smartphones,…

cs.CL2024

The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends

Mengqi Chen, Bin Guo, Hao Wang +6

Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. We humans te…

cs.DC2024★ 10 cited

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control

Xiaochen Li, Sicong Liu, Zimu Zhou +3

The rise of mobile devices with abundant sensory data and local computing capabilities has driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) e…

cs.DC2023

AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing

Bowen Pang, Sicong Liu, Hongli Wang +6

With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensiv…