4 papers
Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction
Wenjie Ding, Yi Sin Lin, Jiale Liu +8
Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange creates a communication bot-tl…
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Xiaobo Xia, Xiaofeng Liu, Jiale Liu +5
Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality…
Exposing Privacy Risks in Graph Retrieval-Augmented Generation
Jiale Liu, Jiahao Zhang, Suhang Wang
Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with external, up-to-date knowledge. Graph RAG has emerged as an advanced pa…
Federated Class-Incremental Learning with Prompting
Xin Luo, Fang-Yi Liang, Jiale Liu +3
As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread atte…