2 citations · 2 across the 5 of their papers we have counts for
8 papers
BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition
Zhiyuan Wang, Yuezhi Zhou, Ziqi Zhang +3
On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and inte…
MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation
Yizhe Zhao, Yongjian Fu, Zihao Feng +4
Mobile advertising dominates app monetization but introduces risks ranging from intrusive user experience to malware delivery. Existing detection methods rely either on static anal…
Gains: Fine-grained Federated Domain Adaptation in Open Set
Zhengyi Zhong, Wenzheng Jiang, Weidong Bao +5
Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios,…
CSVAR: Enhancing Visual Privacy in Federated Learning via Adaptive Shuffling Against Overfitting
Zhuo Chen, Zhenya Ma, Yan Zhang +8
Although federated learning preserves training data within local privacy domains, the aggregated model parameters may still reveal private characteristics. This vulnerability stems…
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning
Ziqing Qiao, Yongheng Deng, Jiali Zeng +7
Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs, increasing computational over…
AugFL: Augmenting Federated Learning with Pretrained Models
Sheng Yue, Zerui Qin, Yongheng Deng +3
Federated Learning (FL) has garnered widespread interest in recent years. However, owing to strict privacy policies or limited storage capacities of training participants such as I…
Federated Offline Policy Optimization with Dual Regularization
Sheng Yue, Zerui Qin, Xingyuan Hua +2
Federated Reinforcement Learning (FRL) has been deemed as a promising solution for intelligent decision-making in the era of Artificial Internet of Things. However, existing FRL ap…
Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer
Yongheng Deng, Ziqing Qiao, Ju Ren +2
While large language models (LLMs) are empowered with broad knowledge, their task-specific performance is often suboptimal. It necessitates fine-tuning LLMs with task-specific data…