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From the 1 of 43 linked papers with an AI index.

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20242026
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cs.LG2026

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

Qijun Hou, Yuchen Shi, Pingyi Fan +1

Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods assume full visibility of all c…

cs.LG2026

FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference

Qijun Hou, Yuchen Shi, Pingyi Fan +1

Federated Learning (FL) facilitates collaborative model training across decentralized clients while preserving data privacy by avoiding raw data exchange. Despite its potential, FL…

cs.LG2026

Personalized Federated Distillation Assisted Vehicle Edge Caching Strategy

Xun Li, Qiong Wu, Pingyi Fan +3

Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge no…

cs.LG2026

U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning

Yiang Wu, Qiong Wu, Pingyi Fan +4

This demonstration presents U-Parking, a distributed Ultra-Wideband (UWB)-assisted autonomous parking system. By integrating Large Language Models (LLMs)-assisted planning with rob…

cs.LG2026

EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks

Yuchen Shi, Qijun Hou, Pingyi Fan +1

Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. Howev…

cs.LG2025

Semantic-Aware Cooperative Communication and Computation Framework in Vehicular Networks

Jingbo Zhang, Maoxin Ji, Qiong Wu +3

Semantic Communication (SC) combined with Vehicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of Vehicles (IoV). Focusing on highway sce…