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20242026
most citedThreats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges

33 citations · 34 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization

Huiran Duan, Qian Zhou, Zhongliang Guo +4

Conventional gait de-identification methods often encounter an inherent trade-off: they either provide insufficient identity suppression or introduce spatiotemporal distortions tha…

cs.CV2026

PoseCompass: Intelligent Synthetic Pose Selection for Visual Localization

Yanan Zhou, Zhaoyan Qian, Yanli Li +3

In visual localization, Absolute Pose Regression (APR) enables real-time 6-DoF camera pose inference from single images, yet critically depends on fine-tuning data quality and cove…

cs.HC2026

GazeSync: A Mobile Eye-Tracking Tool for Analyzing Visual Attention on Dynamically Manipulated Content

Yaxiong Lei, Rishab Talwar, Shijing He +5

Conventional mobile eye-tracking maps gaze to static screen coordinates, failing to capture user attention when content is dynamic. As users pinch, zoom, and rotate images, static…

cs.LG2025

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…

cs.LG20251 cited

Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection

Yuqi Li, Xingyou Lin, Yanli Li +6

As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional multimedia data, and federated learn…

cs.DC202433 cited

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…