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From the 1 of 22 linked papers with an AI index.

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20242026
most citedLarge Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions

3 citations · 3 across the 11 of their papers we have counts for

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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

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

Federated Learning With Energy Harvesting Devices: An MDP Framework

Kai Zhang, Xuanyu Cao, Khaled B. Letaief

Federated learning (FL) necessitates that edge devices conduct local training and communicate with a parameter server, resulting in significant energy consumption. A key challenge…

cs.LG2025

Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies

Yuanming Shi, Jingyang Zhu, Chunxiao Jiang +2

Driven by the growing demand for intelligent remote sensing applications, large artificial intelligence (AI) models pre-trained on large-scale unlabeled datasets and fine-tuned for…