ART4FL: An Agent-Based Architectural Approach for Trustworthy Federated Learning in the IoT

Alkhabbas, Fahed; Alawadi, Sadi; Ayyad, Majed; Spalazzese, Romina; Davidsson, Paul

doi:10.1109/fmec59375.2023.10306036

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…This is due to the fact that it does not contain any outliers. Nevertheless, it is widely known from [43,45,46], that SMAPE is very sensitive and depends heavily on the level of time series and on the existence of extremely small actual values (close to zero). This is why we observe worse performance both for CPU Polarized Workload and CPU Increasing Load with Fluctuations for the SMAPE accuracy metric.…”

Section: Discussionmentioning

confidence: 99%

“…In future work, we will also examine possible encryption methods that may apply to the trained local model parameters that are exchanged among cloud clients and the central cloud servers of the FL system to avoid man-in-the-middle attacks, data leakages, or other similar cyberattacks. Moreover, there could exist a trustworthy federation schema based on various concepts such as the trust scores of various nodes of the FL system in a safe network [46,47]. The importance of the above observations motivates the following open questions:…”

Section: Discussionmentioning

confidence: 99%

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MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud Environments

Stefanidis,

Verginadis,

Mentzas

2023

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Cloud computing and relevant emerging technologies have presented ordinary methods for processing edge-produced data in a centralized manner. Presently, there is a tendency to offload processing tasks as close to the edge as possible to reduce the costs and network bandwidth used. In this direction, we find efforts that materialize this paradigm by introducing distributed deep learning methods and the so-called Federated Learning (FL). Such distributed architectures are valuable assets in terms of efficiently managing resources and eliciting predictions that can be used for proactive adaptation of distributed applications. In this work, we focus on deep learning local loss functions in multi-cloud environments. We introduce the MulticloudFL system that enhances the forecasting accuracy, in dynamic settings, by applying two new methods that enhance the prediction accuracy in applications and resources monitoring metrics. The proposed algorithm’s performance is evaluated via various experiments that confirm the quality and benefits of the MulticloudFL system, as it improves the prediction accuracy on time-series data while reducing the bandwidth requirements and privacy risks during the training process.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud Environments

Stefanidis,

Verginadis,

Mentzas

2023

Information

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“…FL has been successfully applied in heterogeneous IoT environments. Here are some recent case studies and real-world applications: [44].…”

Section: Iotmentioning

confidence: 99%

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

Mengistu,

Kim,

Lin

2024

Sensors

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Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

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A cognitive deep learning approach for medical image processing

Fakhouri,

Alawadi,

Awaysheh

et al. 2024

Sci Rep

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In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

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ART4FL: An Agent-Based Architectural Approach for Trustworthy Federated Learning in the IoT

Cited by 4 publications

References 18 publications

MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud Environments

MulticloudFL: Adaptive Federated Learning for Improving Forecasting Accuracy in Multi-Cloud Environments

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

A cognitive deep learning approach for medical image processing

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