A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Mengistu, Tesfahunegn Minwuyelet; Kim, Taewoon; Lin, Jenn-Wei

doi:10.3390/s24030968

Cited by 6 publications

(5 citation statements)

References 148 publications

(209 reference statements)

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“…Federated learning is designed to be a super artificial intelligent technology entrenched in the Industrial Revolution 4.0 and the current Society 5.0, deployed to execute a well-organized machine learning operation within multi-edge nodes and still maintain a secured and private nature of data [26,27]. Different algorithms are used for machine learning in federated learning.…”

Section: Methodology and System Threat Modelmentioning

confidence: 99%

“…This time lag, T ′ , requires an optimization of the parameter to compensate for the delay and the weakening of the transmission due to the out-of-network coverage as represented by This parameter time lag incidence T ′ shows the network connectivity strength of the edge node. Hence, to ensure nodes with high T ′ have very reduced parameter weight and limited signal weakening issues, a reduced function speed is identified as a challenge that weakens the signal strength of the parameter weight [9,26]. This is based on the time lag T ′ of the edge node and the speed of reduced function q, which is set between 0 and 1.…”

Section: Asynchronous Federated Learning Dual-weighted Modification P...mentioning

confidence: 99%

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Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Odeh,

Yang,

Nwakanma

et al. 2024

Electronics

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The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

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Section: Methodology and System Threat Modelmentioning

confidence: 99%

Section: Asynchronous Federated Learning Dual-weighted Modification P...mentioning

confidence: 99%

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Odeh,

Yang,

Nwakanma

et al. 2024

Electronics

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“…Figure 2 shows an example that illustrates the impact of non-IID data on the global model and how the model diverges from the optimal case; while the global model built using IID data is close to the optimal case, in the non-IID data case, the model will need more communication rounds to converge and reach the same accuracy as models trained using IID data. Therefore, the non-IID data problem must be solved to enhance the model's performance [5,10,11]. Another challenge in FL is communication; the communication between clients and the central server is considered a bottleneck due to the issue of limited bandwidth in the network and the number of communication rounds as clients in FL train local models and share them with the central server in repetitive rounds [12,13].…”

Section: Introductionmentioning

confidence: 99%

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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“…The development of the Internet of Things (IoT) is driving a significant increase in smart and connected devices, opening up opportunities for new applications and services [ 1 ]. One such application area is healthcare, where the development of IoT technology aims to provide efficient services to patients [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder

Bhatti,

Choi

2024

Sensors

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The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the training of neural networks while maintaining the privacy of the data. To integrate federated learning into IoT healthcare, hospitals must be part of the network to jointly train a global central model on the server. Local hospitals can train the global model using their patient datasets and send the trained localized models to the server. These localized models are then aggregated to enhance the global model training process. The aggregation of local models dramatically influences the performance of global training, mainly due to the heterogeneous nature of patient data. Existing solutions to address this issue are iterative, slow, and susceptible to convergence. We propose two novel approaches that form groups efficiently and assign the aggregation weightage considering essential parameters vital for global training. Specifically, our method utilizes an autoencoder to extract features and learn the divergence between the latent representations of patient data to form groups, facilitating more efficient handling of heterogeneity. Additionally, we propose another novel aggregation process that utilizes several factors, including extracted features of patient data, to maximize performance further. Our proposed approaches for group formation and aggregation weighting outperform existing conventional methods. Notably, significant results are obtained, one of which shows that our proposed method achieves 20.8% higher accuracy and 7% lower loss reduction compared to the conventional methods.

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A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Cited by 6 publications

References 148 publications

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

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

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder

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