Fang Shi

FedPAP: Federated Learning With Personalized Adaptive Pruning

Dongdong Li, Weiwei Lin, Fang Shi, Yunfei Peng et autres

Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, but faces fundamental challenges from statistical het erogeneity, communication bottlenecks, and diverse device capabilities in wireless networks. Existing personalized FL approaches either maintain uniform model architectures or …

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Decentralized Federated Learning With Period Gradient Tracking Over Time-Varying Networks

Fang Shi, Yuehong Chen, Qiong Huang, Tiansheng Huang et autres

To address the communication challenges associated with Federated Learning (FL), Decentralized Federated Learning (DFL) eliminates the central server and trains the model with decentralized method, enabling each client to only communicate with its neighbors. However, per our analysis, model trained with DFL …

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FSGDR: A Cognitive Embodied Agent Based on Fuzzy Semantic-Guided Dual-Mode Reasoning

Fang Shi, Jiayin Zhang, JianKang Zeng, Shunpu Tang et autres

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ReSO: A Real-Time Scalable Offloading Approach for Dynamic Vehicle-to-Everything Systems

Peng Peng, Weiwei Lin, Wentai Wu, Fang Shi et autres

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Prodigal: Backdoor defense for federated learning beyond robust aggregation

Guozhi Liu, Weiwei Lin, Tiansheng Huang, Fang Shi et autres

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AdaptiveFL: Communication-Adaptive Federated Learning Under Dynamic Bandwidth

Guozhi Liu, Weiwei Lin, Tiansheng Huang, Fang Shi et autres

Federated learning (FL) is a distributed machine learning paradigm that enables heterogeneous devices to train a model collaboratively. Recognizing communication as a bottleneck in FL, existing communication-efficient solutions, e.g., HeteroFL and LotteryFL, etc., utilize gradient sparsification to reduce communication costs. However, existing …

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Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing

Haijie Wu, Weiwei Lin, Haotong Zhang, Fang Shi et autres

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a …

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Dynamic Client Selection for Over-the-Air Federated Learning Network

Fang Shi, Weiwei Lin, Chaoda Peng, Cankun Zhong et autres

As a privacy-preserving solution, federated learning (FL) demonstrates great potential in distributed model training, but limited bandwidth, particularly in near-field communication (NFC)-based systems, emerges as a key bottleneck by restricting the number of participating clients. To address this challenge, over-the-air FL leverages …

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K-Core Structure Feature Encoding-Based Enhanced Federated Graph Learning Framework

Dongdong Li, C. H. Yang, Fang Shi, Weiwei Lin

Federated Graph Learning (FGL) demonstrates tremendous potential in distributed graph data analysis and modeling. The rapid growth of graph data and the increasing awareness of privacy protection make FGL research highly valuable. However, its development faces two critical challenges: the non-IID problem …

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Adaptive Incremental Broad Learning System Based on Interval Type-2 Fuzzy Set With Automatic Determination of Hyperparameters

Haijie Wu, Weiwei Lin, Yuehong Chen, Fang Shi et autres

The fuzzy broad learning system (FBLS) has received increasing attention due to its ability to quickly train from broad learning systems (BLS) and interpretability with fuzzy inference. However, the randomness of BLS brings instability to the training performance of the model, so …

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Recognition of drill string vibration state based on WGAN-div and CNN-IWPSO-SVM

Fengtao Qu, Hualin Liao, Ming Lu, Wenlong Niu et autres

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The Analysis and Optimization of Volatile Clients in Over-the-Air Federated Learning

Fang Shi, Weiwei Lin, Xiumin Wang, Keqin Li et autres

This paper investigates the implementation of Federated Learning (FL) in an over-the-air computation system with volatile clients, where each client operates under a limited energy budget and may unexpectedly drop out during local training sessions. The dropout of clients not only wastes …

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