9 papers
SpatialBench: Is Your Spatial Foundation Model an All-Round Player?
Haosong Peng, Hao Li, Jiaqi Chen +10
While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing…
Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs
Yuxuan Liu, Wenchao Xu, Haozhao Wang +5
Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods a…
PrismWF: A Multi-Granularity Patch-Based Transformer for Robust Website Fingerprinting Attack
Yuhao Pan, Wenchao Xu, Fushuo Huo +3
Tor is a low-latency anonymous communication network that protects user privacy by encrypting website traffic. However, recent website fingerprinting (WF) attacks have shown that e…
HALO: Semantic-Aware Distributed LLM Inference in Lossy Edge Network
Peirong Zheng, Wenchao Xu, Haozhao Wang +2
The deployment of large language models' (LLMs) inference at the edge can facilitate prompt service responsiveness while protecting user privacy. However, it is critically challeng…
Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
Senyao Li, Haozhao Wang, Wenchao Xu +6
As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost,…
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Yichen Li, Haozhao Wang, Wenchao Xu +9
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-st…