10 papers
Device-Cloud Collaborative LLM Inference with Multi-Modal, Multi-Task, Multi-Turn Conversations
Liangqi Yuan, Dong-Jun Han, Shiqiang Wang +1
Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multi-modal data sources and multi-turn con…
Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating
Evan Chen, Shiqiang Wang, Kevin S Chan +2
Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained routers or collaboration-aware f…
Agentic Performance at the Edge: Insights from Benchmarking
Shiqiang Wang, Herbert Woisetschläger
Agentic artificial intelligence (AI) is a natural fit for Internet of Things (IoT) and edge systems, but edge deployments are often constrained to models around 8 billion parameter…
PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +1
Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents preserve privacy at the cost of over…
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2
Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a gro…
A Hierarchical Gradient Tracking Algorithm for Mitigating Subnet-Drift in Fog Learning Networks
Evan Chen, Shiqiang Wang, Christopher G. Brinton
Federated learning (FL) encounters scalability challenges when implemented over fog networks that do not follow FL's conventional star topology architecture. Semi-decentralized FL…