Communication-Efficient Federated Indoor Localization with Layerwise Swapping Training-FedAvg

LIANG, Jinjie; LIU, Zhenyu; Zhou, Zhiheng; Xu, Yan

doi:10.1587/transfun.2022eap1004

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…In Fed-SGD, each client uploads its model to the server after one iteration, and the server performs weighted averaging to update the global model [21]. On the other hand, in Fed-Avg, the server allows each client to perform multiple local iterations before uploading the model for the next aggregation step, based on the predetermined number of rounds [22]. Fed-Avg is particularly suitable for AIoT, for adapting to the situation that AIoT devices have varying processing capabilities.…”

Section: Fl In Idsmentioning

confidence: 99%

A novel federated learning aggregation algorithm for AIoT intrusion detection

Jia,

Lin,

Sun

2024

IET Communications

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Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg.

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Section: Fl In Idsmentioning

confidence: 99%

A novel federated learning aggregation algorithm for AIoT intrusion detection

Jia,

Lin,

Sun

2024

IET Communications

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Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

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Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client’s limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers.

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Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey

Wang

et al. 2023

Sensors

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For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and calculate localizations and poses relative to their neighbors distributively, are highly desired. Distributed relative localization has the advantages of a low communication burden and better system robustness but encounters challenges in the distributed algorithm design, communication protocol design, local network organization, etc. This paper presents a detailed survey of the key methodologies designed for distributed relative localization for robot networks. We classify the distributed localization algorithms regarding to the types of measurements, i.e., distance-based, bearing-based, and multiple-measurement-fusion-based. The detailed design methodologies, advantages, drawbacks, and application scenarios of different distributed localization algorithms are introduced and summarized. Then, the research works that support distributed localization, including local network organization, communication efficiency, and the robustness of distributed localization algorithms, are surveyed. Finally, popular simulation platforms are summarized and compared in order to facilitate future research and experiments on distributed relative localization algorithms.

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Communication-Efficient Federated Indoor Localization with Layerwise Swapping Training-FedAvg

Cited by 3 publications

References 32 publications

A novel federated learning aggregation algorithm for AIoT intrusion detection

A novel federated learning aggregation algorithm for AIoT intrusion detection

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey

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