Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by...
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Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.
Since the first international conference on urban air quality, held at the University ofHertfordshire in 1996, significant advances have taken place in the field of urban air pollution. In addition to the scientific a...
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ISBN:
(数字)9789401009324
ISBN:
(纸本)9780792366768;9789401037969
Since the first international conference on urban air quality, held at the University ofHertfordshire in 1996, significant advances have taken place in the field of urban air pollution. In addition to the scientific advances in the measurement, modelling and management of urban air quality, significant progress has been achieved in relation to the establishment of major frameworks to ensure a more effective mechanism for international collaboration. Two such frameworks are SATURN (Studying Atmospheric Pollution in Urban Areas) and TRAPOS (Optimisation of Modelling Methods for Traffic Pollution in Streets). In response to such advances, the second international conference was held at the Technical University of Madrid in March 1999 with active participation of SATURN and TRAPOS investigators. The organisation of the conference was headed by the Institute of Physics in collaboration with the Technical University of Madrid and the University of Hertfordshire. The support of IUAPPA and AWMA ensured a truly worldwide promotion and participation. The meeting attracted 140 scientists from 26 different countries establishing it as a major forum for exchanging and discussing the latest research fmdings in this field.
This volume constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2014, and Ontologies, Databases, and Applications of Semantics, ODBASE 2014, held...
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ISBN:
(数字)9783662455630
ISBN:
(纸本)9783662455623
This volume constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2014, and Ontologies, Databases, and Applications of Semantics, ODBASE 2014, held as part of OTM 2014 in October 2014 in Amantea, Italy.
The 39 full papers presented together with 12 short papers and 5 keynotes were carefully reviewed and selected from a total of 115 submissions. The OTM program covers subjects as follows: process designing and modeling, process enactment, monitoring and quality assessment, managing similarity, software services, improving alignment, collaboration systems and applications, ontology querying methodologies and paradigms, ontology support for web, XML, and RDF data processing and retrieval, knowledge bases querying and retrieval, social network and collaborative methodologies, ontology-assisted event and stream processing, ontology-assisted warehousing approaches, ontology-based data representation, and management in emerging domains.
One of the main challenges for underwater applications, such as environmental monitoring and disaster management, is achieving efficient data transmission in environments where conditions change rapidly, and resources...
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One of the main challenges for underwater applications, such as environmental monitoring and disaster management, is achieving efficient data transmission in environments where conditions change rapidly, and resources need for data transport are scarce. The capability of evaluating the Value of information (VoI) enables us to assess these problems by proposing a Value of Information-based Situation-Aware Non-Linear Routing (VoI SANLR/VoI SANL) method. It aims to deal with critical event scenarios using BDI (Belief-Desire-Intention) logic criteria and prioritizing the timely uploading of data-driven information towards the destination. SANLR of VoI is developed to reduce energy consumption, end-to-end latency, jitter, and improve Packet Delivery Ratio (PDR) in underwater communication networks. VoI SANLR introduces principles of priority-based methods and intends to address challenges in terms of underwater environment such as varying channel conditions, lack energy resources, and real-time decision requirements by using SANLR. Energy optimization analysis reveals consistent outperformance, achieving a remarkable 95% reduction in energy consumption compared to other techniques. Low latency is maintained, ranging from 2.5 to 0.5 seconds, showcasing enhanced efficiency and scalability. VoI SANLR demonstrates exceptional performance in both throughput and jitter. It achieves the highest data transfer rates, ranging from 100 kbps to 110 kbps, indicating outstanding efficiency. Additionally, the jitter remains consistently low, between 1.8 ms and 2 ms, ensuring minimal delay variability and improved communication stability. PDR consistently surpasses other techniques, reaching a maximum of 99%. Additionally, network lifetime analysis demonstrates VoI SANLR's superiority, exhibiting the highest network lifetime at each node and a significant 31.25% improvement at Node 100 compared to other methods.
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