The distributed computing continuum systems (DCCS) and representation learning (ReL) are two diverse computer science technologies with their use cases, applications, and benefits. The DCCS helps increase flexibility with improved performance of hybrid IoT-Edge-Cloud infrastructures. In contrast, representation learning extracts the features (meaningful information) and underlying explanatory factors from the given datasets. With these benefits, using ReL for DCCS to improve its performance by monitoring the devices will increase the utilization efficiency, zero downtime, etc. In this context, this paper discusses the promising role of ReL for DCCS in terms of different aspects, including device condition monitoring, predictions, management of the systems, etc. This paper also provides a list of ReL algorithms and their pitfalls which helps DCCS by considering various constraints. In addition, this paper list different challenges imposed on ReL to analyze DCCS data. It also provides future research directions to make the systems autonomous, performing multiple tasks simultaneously with the help of other AI/ML approaches.

The Promising Role of Representation Learning for Distributed Computing Continuum Systems

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT) and machine learning technologies have been widely used to achieve the intelligent control and routing planning for ships. As an important branch of machine learning, federated learning is the first choice to train an accurate joint model without sharing ships' data directly. However, there are still many unsolved challenges while using federated learning in IoT-enabled MTS, such as the privacy preservation and Byzantine attacks. To surmount the above challenges, a novel mechanism, namely DisBezant, is designed to achieve the secure and Byzantine-robust federated learning in IoT-enabled MTS. Specifically, a credibility-based mechanism is proposed to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships. The credibility is introduced to measure the trustworthiness of uploaded knowledge from ships and is updated based on their shared information in each epoch. Then, we design an efficient privacy-preserving gradient aggregation protocol based on a secure two-party calculation protocol. With the help of a central server, we can accurately recognise the Byzantine attackers and update the global model parameters privately. Furthermore, we theoretically discussed the privacy preservation and efficiency of DisBezant. To verify the effectiveness of our DisBezant, we evaluate it over three real datasets and the results demonstrate that DisBezant can efficiently and effectively achieve the Byzantine-robust federated learning. Although there are 40% nodes are Byzantine attackers in participants, our DisBezant can still recognise them and ensure the accurate model training. 

DisBezant: Secure and Robust Federated Learning Against Byzantine Attack in IoT-Enabled MTS

With the intelligentization of Maritime Transportation System (MTS), Internet of Thing (IoT) and machine learning technologies have been widely used to achieve the intelligent control and routing planning for ships. As an important branch of machine learning, federated learning is the first choice to train an accurate joint model without sharing ships’ data directly. However, there are still many unsolved challenges while using federated learning in IoT-enabled MTS, such as the privacy preservation and Byzantine attacks. To surmount the above challenges, a novel mechanism, namely DisBezant, is designed to achieve the secure and Byzantine-robust federated learning in IoT-enabled MTS. Specifically, a credibility-based mechanism is proposed to resist the Byzantine attack in non-iid (not independent and identically distributed) dataset which is usually gathered from heterogeneous ships. The credibility is introduced to measure the trustworthiness of uploaded knowledge from ships and is updated based on their shared information in each epoch. Then, we design an efficient privacy-preserving gradient aggregation protocol based on a secure two-party calculation protocol. With the help of a central server, we can accurately recognise the Byzantine attackers and update the global model parameters privately. Furthermore, we theoretically discussed the privacy preservation and efficiency of DisBezant. To verify the effectiveness of our DisBezant, we evaluate it over three real datasets and the results demonstrate that DisBezant can efficiently and effectively achieve the Byzantine-robust federated learning. Although there are 40% nodes are Byzantine attackers in participants, our DisBezant can still recognise them and ensure the accurate model training.

The distributed computing continuum systems (DCCS) and representation learning (ReL) are two diverse computer science technologies with their use cases, applications, and benefits. The DCCS helps increase flexibility with improved performance of hybrid IoT-Edge-Cloud infrastructures. In contrast, representation learning extracts the features (meaningful information) and underlying explanatory factors from the given datasets. With these benefits, using ReL for DCCS to improve its performance by monitoring the devices will increase the utilization efficiency, zero downtime, etc. In this context, this paper discusses the promising role of ReL for DCCS in terms of different aspects, including device condition monitoring, predictions, management of the systems, etc. This paper also provides a list of ReL algorithms and their pitfalls which helps DCCS by considering various constraints. In addition, this paper list different challenges imposed on ReL to analyze DCCS data. It also provides future research directions to make the systems autonomous, performing multiple tasks simultaneously with the help of other AI/ML approaches. 

Abstract Distributed computing continuum systems (DCCS) make use of a vast number of computing devices to process data generated by edge devices such as the Internet of Things and sensor nodes. Besides performing computations, these devices also produce data including, for example, event logs, configuration files, network management information. When these data are analyzed, we can learn more about the devices, such as their capabilities, processing efficiency, resource usage, and failure prediction. However, these data are available in different forms and have different attributes due to the highly heterogeneous nature of DCCS. The diversity of data poses various challenges which we discuss by relating them to big data, so that we can utilize the advantages of big data analytical tools. We enumerate several existing tools that can perform the monitoring task and also summarize their characteristics. Further, we provide a general governance and sustainable architecture for DCCS, which reflects the human body’s self-healing model. The proposed model has three stages: first, it analyzes system data to acquire knowledge; second, it can leverage the knowledge to monitor and predict future conditions; and third, it takes further actions to autonomously solve any issue or to alert administrators. Thus, the DCCS model is designed to minimize the system’s downtime while optimizing resource usage. A small set of data is used to illustrate the monitoring and prediction of the performance of a system through Bayesian network structure learning. Finally, we discuss the limitations of the governance and sustainability model, and we provide possible solutions to overcome them and make the system more efficient. 

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Governance and sustainability of distributed continuum systems: a big data approach

Because of the powerful computing and storage capability in cloud computing, machine learning as a service (MLaaS) has recently been valued by the organizations for machine learning training over s...

KumariSaru

Papers

Privacy-Preserving Distributed Multi-Task Learning against Inference Attack in Cloud Computing