arXiv Computer Vision
MPT: Missing Prototype Tracking via Barycentric Reconstruction in Vehicular Federated Learning
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arXiv Machine Learning
Sep 4
Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks
The paper investigates how federated learning (FL) updates in vehicular edge networks can reveal client identities through gradient-based attacks on inertial sensor data, using the UCI Human Activity Recognition benchmark as a proxy. Experiments show that an honest-but-curious server can identify clients with near-perfect accuracy from unprotected updates. The authors evaluate lightweight defenses—clipping followed by Gaussian noise and ensemble FL—to mitigate this privacy risk while preserving model utility, reporting differential‑privacy budgets and empirical results across multiple attack classifiers and data partitions.
arXiv Machine Learning
Jun 8
Federated Foundation Models over Vehicular Networks
arXiv:2606. 06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL).
arXiv Computer Vision
Oct 2
FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains
FedCKA introduces a Centered Kernel Alignment (CKA)-based method for federated 3D perception that dynamically balances personalization and globalization. By computing layer-wise feature similarities between local client models and a global consensus model, FedCKA generates client‑specific aggregation masks to selectively share representation‑consistent layers. Experiments on a unified multi‑domain nuScenes benchmark demonstrate that FedCKA surpasses established federated baselines, improving average NDS by 7 percentage points.
arXiv Machine Learning
Sep 17
FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels
FedPGT introduces a progressive gradient transmission scheme for vehicular federated learning over time‑varying channels, where vehicles send high‑magnitude gradient entries according to instantaneous channel conditions. The authors derive a convergence bound showing diminishing returns governed by a power‑law decay, and formulate a stochastic optimization problem that is solved via a Lyapunov drift‑plus‑penalty approach with per‑slot surrogate variables. A low‑complexity resource allocation algorithm is proposed, and experiments on CIFAR‑10 and Argoverse demonstrate a 3.65% accuracy gain and a 12.66% reduction in displacement error compared to state‑of‑the‑art baselines.
arXiv Computer Vision
Sep 30
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
arXiv:2503.12232v3 Announce Type: replace Abstract: Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensi...
arXiv Machine Learning
Sep 17
Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking
The paper introduces GrayTrack, a vehicle‑tracking system that fuses weak, indirect observations from third‑party sensors with sparse direct sensor data using a road‑constrained particle filter. Experiments on a CARLA‑Mininet‑WiFi pipeline show that the system achieves an F1 score of 0.989 for anonymous vehicle passages and reduces trajectory RMSE by 60.1% while cutting catastrophic track loss from 35.8% to 0.3%. These results demonstrate that incorporating indirect third‑party observations can substantially extend tracking capabilities when direct sensor access is limited.