1 citations · 1 across the 7 of their papers we have counts for
8 papers
From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health
He Hu, Yucheng Zhou, Qianning Wang +8
The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, high cost, stigma, and privacy c…
Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents
Junliang Liu, Ruoyu Li, Wenxin Tang +4
LLM agents increasingly rely on third-party skills, using natural-language descriptions for selection and instruction bodies for planning. This progressive-disclosure design expose…
Cerberus: Efficient Inference with Adaptive Parallel Decoding and Sequential Knowledge Enhancement
Yuxuan Liu, Wenyuan Li, Laizhong Cui +1
Large language models (LLMs) often face a bottleneck in inference speed due to their reliance on auto-regressive decoding. Recently, parallel decoding has shown significant promise…
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma, Yipeng Zhou, Qi Li +3
To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To…
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Huitong Jin, Yipeng Zhou, Quan Z. Sheng +2
Differentially Private Federated Learning (DPFL) strengthens privacy protection by perturbing model gradients with noise, though at the cost of reduced accuracy. Although prior emp…
Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming
Lei Zhang, Tao Long, Weizhen Xu +2
Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of t…
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Xiaoxin Su, Yipeng Zhou, Laizhong Cui +1
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates…
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
Xiaoxin Su, Yipeng Zhou, Laizhong Cui +2
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model di…