33 citations · 39 across the 8 of their papers we have counts for
8 papers
mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar
Jianyi Zhou, Chenghao Zhang, Yanli Li +1
Indoor mmWave radar perception is difficult to reproduce because measured range-angle responses depend on scene geometry, material response, multipath, hardware conventions, and si…
GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks
Yanli Li, Yanan Zhou, Zhongliang Guo +6
Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors that compromise model utility or fairness across sen…
Optimal Look-back Horizon for Time Series Forecasting in Federated Learning
Dahao Tang, Nan Yang, Yanli Li +3
Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in the federated learning scenarios where data is decentra…
DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
Yuning Zhang, Grant Pinkert, Nan Yang +2
Large Language Models (LLMs) are increasingly deployed as Internet/Web services (LLM-as-a-Service) with strict latency Service-Level Objectives (SLOs) under tight GPU memory budget…
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Yanli Li, Zhongliang Guo, Nan Yang +3
Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks…
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
Yanli Li, Jehad Ibrahim, Huaming Chen +2
A large number of federated learning (FL) algorithms have been proposed for different applications and from varying perspectives. However, the evaluation of such approaches often r…
Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios
Yanli Li, Huaming Chen, Wei Bao +2
Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to…
Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions
Zhengmeng Xu, Yujie Wang, Xiaotong Feng +3
We propose a time series forecasting method named Quantum Gramian Angular Field (QGAF). This approach merges the advantages of quantum computing technology with deep learning, aimi…