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Olsson, Helena HolmströmORCID iD iconorcid.org/0000-0002-7700-1816
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Publikasjoner (10 av 142) Visa alla publikasjoner
Dzhusupova, R., Bosch, J. & Olsson, H. H. (2024). Choosing the right path for AI integration in engineering companies: A strategic guide. Journal of Systems and Software, 210, Article ID 111945.
Åpne denne publikasjonen i ny fane eller vindu >>Choosing the right path for AI integration in engineering companies: A strategic guide
2024 (engelsk)Inngår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 210, artikkel-id 111945Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Machine learning, Deep learning, Artificial intelligence, Developing and deploying AI project, Engineering procurement and construction, industry
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-66157 (URN)10.1016/j.jss.2023.111945 (DOI)001152187200001 ()2-s2.0-85182456889 (Scopus ID)
Tilgjengelig fra: 2024-02-27 Laget: 2024-02-27 Sist oppdatert: 2024-02-27bibliografisk kontrollert
John, M. M., Gillblad, D., Olsson, H. H. & Bosch, J. (2023). Advancing MLOps from Ad hoc to Kaizen. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Advancing MLOps from Ad hoc to Kaizen
2023 (engelsk)Inngår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64891 (URN)10.1109/seaa60479.2023.00023 (DOI)979-8-3503-4235-2 (ISBN)979-8-3503-4236-9 (ISBN)
Konferanse
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Tilgjengelig fra: 2024-01-09 Laget: 2024-01-09 Sist oppdatert: 2024-01-09bibliografisk kontrollert
Olsson, H. H. & Bosch, J. (2023). All data is equal or is some data more equal? On strategic data collection and use in the embedded systems domain. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>All data is equal or is some data more equal? On strategic data collection and use in the embedded systems domain
2023 (engelsk)Inngår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Effective collection and use of data is key for companies across domains and it is only increasing in importance. For companies in the embedded systems domain, data constitutes the basis not only for quality assurance and diagnostics of their systems but also for new service development and innovation. For these companies, data is an enabler for continuous delivery of customer value and hence, a key asset for entirely new and recurring revenue streams. However, effective use of data requires careful collection of different kinds of data depending on the purpose and context for which it is intended to be used. In this paper, we identify the challenges that companies experience in their contemporary data practices and we outline the kinds of data that companies need to collect as they evolve through different maturity stages. In addition, we provide concrete guidance on the specific data to collect during each maturity stage.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64898 (URN)10.1109/SEAA60479.2023.00056 (DOI)979-8-3503-4235-2 (ISBN)979-8-3503-4236-9 (ISBN)
Konferanse
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Tilgjengelig fra: 2024-01-09 Laget: 2024-01-09 Sist oppdatert: 2024-01-09bibliografisk kontrollert
Hegazy, S., Elsner, C., Bosch, J. & Olsson, H. H. (2023). Analytics and Data-Driven Methods and Practices in Platform Ecosystems: a systematic literature review. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Analytics and Data-Driven Methods and Practices in Platform Ecosystems: a systematic literature review
2023 (engelsk)Inngår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The emergence of platform ecosystems has transformed the business landscape in many industries, giving rise to novel modes of interorganizational cooperation and value co-creation, as well as unconventional challenges. The vast traces of data generated by platform ecosystems makes them ripe for the use of analytics and data-driven methods aimed at improving their health, performance, business outcomes, and evolution. However, the research on the application of analytics within platform ecosystems is limited and spread across multiple disciplines. To address this gap, we conducted a systematic literature review on the application of analytics and data-driven methods and practices within platform ecosystems. A total of 56 studies were reviewed, and underwent data extraction, analysis, and synthesis processes. In addition to presenting themes and patterns in the recent and relevant literature on platform ecosystems analytics, our review offers the following outcomes: an actionable overview of the analytics toolbox currently used within platform ecosystems—spanning domains such as machine learning, deep learning, data science, modelling, simulation, among others—; a roadmap for practitioners to achieve analytics maturity; and a summary of underexplored research areas.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64897 (URN)10.1109/SEAA60479.2023.00018 (DOI)979-8-3503-4235-2 (ISBN)979-8-3503-4236-9 (ISBN)
Konferanse
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Tilgjengelig fra: 2024-01-09 Laget: 2024-01-09 Sist oppdatert: 2024-01-09bibliografisk kontrollert
Fredriksson, T., Bosch, J. & Olsson, H. H. (2023). Classification of Complex-Valued Radar Data using Semi-Supervised Learning: a Case Study. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Classification of Complex-Valued Radar Data using Semi-Supervised Learning: a Case Study
2023 (engelsk)Inngår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In recent years, the interest in applying machine learning (ML) and deep learning (DL) has been increasing due to their ability to learn to predict and find structure in data. The most common approach of ML and DL is supervised learning. Supervised learning requires the input data to be labeled. However, as reported by many industries, such as the embedded systems domain, fully labeled datasets are difficult to obtain since data labeling is manually intensive. This paper uses a semi-supervised learning approach on real-world Pulse-Doppler data obtained from our industry collaborator Saab to address this challenge. We took inspiration from the FixMatch algorithm. To investigate whether unlabeled data can help improve classification accuracy, we compare FixMatch to a supervised baseline. We use five different settings for the number of available labels per class label to investigate how many labeled instances and how much manual effort is required for optimal accuracy. Bayesian Linear Regression is used to analyze the results. The results show that FixMatch can reach a higher accuracy than the supervised baseline. Furthermore, FixMatch requires more computation time but will help reduce manual effort. In addition, FixMatch will not underfit or overfit. Thanks to this study, practitioners know the benefits of utilizing FixMatch and when it is safe to use to improve a supervised baseline in the industry.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64895 (URN)10.1109/SEAA60479.2023.00024 (DOI)979-8-3503-4235-2 (ISBN)979-8-3503-4236-9 (ISBN)
Konferanse
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Tilgjengelig fra: 2024-01-09 Laget: 2024-01-09 Sist oppdatert: 2024-01-09bibliografisk kontrollert
Dakkak, A., Bosch, J., Olsson, H. H. & Issa Mattos, D. (2023). Continuous deployment in software-intensive system-of-systems. Information and Software Technology, 159, 107200-107200, Article ID 107200.
Åpne denne publikasjonen i ny fane eller vindu >>Continuous deployment in software-intensive system-of-systems
2023 (engelsk)Inngår i: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 159, s. 107200-107200, artikkel-id 107200Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Context:While continuous deployment is popular among web-based software development organizations, adopting continuous deployment in software-intensive system-of-systems is more challenging. On top of the challenges arising from deploying software to a single software-intensive embedded system, software-intensive system-of-systems (SiSoS) add a layer of complexity as new software undergoes an extensive field validation applied to individual components of the SiSoS, as well as the overall SiSoS, to ensure that both legacy and new functionalities are working as desired.

Objectives:This paper aims to study how SiSoS transitions to continuous deployment by exploring how continuous deployment impacts field testing and validation activities, how continuous deployment can be practiced in SiSoS, and to identify the success factors that companies need to consider when transitioning to continuous deployment.

Method:We conducted a case study at Ericsson AB focusing on the embedded software of the Third Generation Radio Access Network (3G RAN). The 3G RAN consists of two large-scale software-intensive embedded systems, representing a simple SiSoS composed of two systems. 3G RAN software was the first to transition to continuous deployment and is used as a reference case for other products within Ericsson AB.

Results:Software deployment, in addition to field testing and validation, have transitioned from being a discrete activity performed at the end of software development to a continuous process performed in parallel to software development. Further, our study reveals an orchestrating approach for software deployment, which allows pre/post validation of legacy behavior and new features in a shorter release and deployment cadence. Furthermore, we identified the essential success factors that organizations should consider when transitioning to continuous deployment.

Conclusion:Transition to continuous deployment, in addition to field testing and validation, shall be considered and planned carefully. In this paper, we provide a set of success factors and orchestration technique that helps organization when transitioning to continuous deployment in the software-intensive embedded system-of-systems context.

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
Continuous deployment, Agile software development, Continuous software engineering, Software-intensive system-of-systems
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64242 (URN)10.1016/j.infsof.2023.107200 (DOI)000959787200001 ()2-s2.0-85150259304 (Scopus ID)
Tilgjengelig fra: 2023-12-11 Laget: 2023-12-11 Sist oppdatert: 2023-12-11bibliografisk kontrollert
Zhang, H., Li, J., Qi, Z., Aronsson, A., Bosch, J. & Olsson, H. H. (2023). Deep Reinforcement Learning for Multiple Agents in a Decentralized Architecture: A Case Study in the Telecommunication Domain. In: 2023 IEEE 20TH INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE COMPANION, ICSA-C: . Paper presented at IEEE 20th International Conference on Software Architecture (ICSA), MAR 13-17, 2023, Aquila, ITALY (pp. 183-186). IEEE COMPUTER SOC
Åpne denne publikasjonen i ny fane eller vindu >>Deep Reinforcement Learning for Multiple Agents in a Decentralized Architecture: A Case Study in the Telecommunication Domain
Vise andre…
2023 (engelsk)Inngår i: 2023 IEEE 20TH INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE COMPANION, ICSA-C, IEEE COMPUTER SOC , 2023, s. 183-186Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Deep reinforcement learning has made significant development in recent years, and it is currently applied not only in simulators and games but also in embedded systems. However, when implemented in a real-world context, reinforcement learning is frequently shown to be unstable and incapable of adapting to realistic situations, particularly when directing a large number of agents. In this paper, we develop a decentralized architecture for reinforcement learning to allow multiple agents to learn optimal control policies on their own devices of the same kind but in varied environments. For such multiple agents, the traditional centralized learning algorithm usually requires a costly or time-consuming effort to develop the best-regulating policy and is incapable of scaling to a large-scale system. To address this issue, we propose a decentralized reinforcement learning algorithm (DecRL) and information exchange scheme for each individual device, in which each agent shares the individual learning experience and information with other agents based on local model training. We incorporate the algorithm into each agent in the proposed collaborative architecture and validate it in the telecommunication domain under emergency conditions, in which a macro base station (BS) is broken due to a natural disaster, and three unmanned aerial vehicles carrying BSs (UAV-BSs) are deployed to provide temporary coverage for missioncritical (MC) users in the disaster area. Based on the findings, we show that the proposed decentralized reinforcement learning algorithm can successfully support multi-agent learning, while the learning speed and service quality can be further enhanced.

sted, utgiver, år, opplag, sider
IEEE COMPUTER SOC, 2023
Serie
IEEE International Conference on Software Architecture Workshops, ISSN 2768-427X
Emneord
Reinforcement Learning, Machine Learning, Software Engineering, Emergency Communication Network, Multi-Agent, Decentralized Architecture
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-61865 (URN)10.1109/ICSA-C57050.2023.00048 (DOI)000990534100032 ()2-s2.0-85159142701 (Scopus ID)978-1-6654-6459-8 (ISBN)
Konferanse
IEEE 20th International Conference on Software Architecture (ICSA), MAR 13-17, 2023, Aquila, ITALY
Tilgjengelig fra: 2023-08-15 Laget: 2023-08-15 Sist oppdatert: 2023-08-15bibliografisk kontrollert
Dakkak, A., Bosch, J. & Olsson, H. H. (2023). DevServOps: DevOps For Product-Oriented Product Service Systems. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>DevServOps: DevOps For Product-Oriented Product Service Systems
2023 (engelsk)Inngår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Companies producing software-intensive products do not only offer products to customers but Product Service Systems (PSS), a combination of the products and services that address customers’ needs. Further, product-related services are key in ensuring customer satisfaction as the service organization represents the company’s interface toward its customers, who operate and use the products. Therefore, while DevOps has been widely adopted in companies developing web-based applications aiming to streamline the Development and Operations activities, the projecting of DevOps as applied in web-based applications to PSS is difficult without considering the role of services. Therefore, based on a two years participant observation case study conducted at a multinational telecommunications systems provider, we propose a new and novel approach called Development-Services-Operations (DevServOps) which incorporates services as a key player facilitating an end-to-end software flow toward customers in one direction and feedback toward developers in the other direction.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64896 (URN)10.1109/SEAA60479.2023.00057 (DOI)979-8-3503-4235-2 (ISBN)979-8-3503-4236-9 (ISBN)
Konferanse
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Tilgjengelig fra: 2024-01-09 Laget: 2024-01-09 Sist oppdatert: 2024-01-09bibliografisk kontrollert
Zhang, H., Li, J., Qi, Z., Aronsson, A., Bosch, J. & Olsson, H. H. (2023). Multi-Agent Reinforcement Learning in Dynamic Industrial Context. In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC): . Paper presented at 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Torino, Italy, June 26-30, 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Multi-Agent Reinforcement Learning in Dynamic Industrial Context
Vise andre…
2023 (engelsk)Inngår i: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings - International Computer Software & Applications Conference, ISSN 0730-3157
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-63756 (URN)10.1109/compsac57700.2023.00066 (DOI)001046484100056 ()2-s2.0-85168859799 (Scopus ID)979-8-3503-2697-0 (ISBN)979-8-3503-2698-7 (ISBN)
Konferanse
47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Torino, Italy, June 26-30, 2023
Tilgjengelig fra: 2023-11-20 Laget: 2023-11-20 Sist oppdatert: 2023-11-20bibliografisk kontrollert
Zhang, H., Bosch, J. & Olsson, H. H. (2023). QuaFedAsync: Quality-based Asynchronous Federated Learning for the Embedded Systems. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA): . Paper presented at 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>QuaFedAsync: Quality-based Asynchronous Federated Learning for the Embedded Systems
2023 (engelsk)Inngår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In recent years, Federated Learning, as an approach to distributed learning, has shown its potential with the increasing number of devices on the edge and the development of computing power. The method enables large-scale training on the device that creates the data but with the sensitive data remaining within the data’s owner. In reality, however, the vast majority of enterprises have the problem of low data volume and poor model quality to support the implementation of Federated Learning methods. Learning quality assurance for edge devices is still the major issue which prevents Federated Learning to be applied in industrial contexts, especially in safety-critical applications. In this paper, we propose a quality-based asynchronous Federated Learning algorithm (QuaFedAsync) to address these challenges. We report on a study in which we used two well-known data sets, i.e., DDAD and KITTI datasets, and validate the proposed algorithm on an industrial use case concerned with monocular depth estimation in the automotive domain. Our results show that the proposed algorithm significantly improves the prediction performance compared to the commonly applied aggregation protocols while maintaining the same level of accuracy as centralized machine learning. Based on the results, we prove the learning efficiency and robustness when applying the algorithm to industrial scenarios.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
Proceedings (EUROMICRO Conference on Software Engineering and Advanced Applications), ISSN 2640-592X, E-ISSN 2376-9521
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64892 (URN)10.1109/SEAA60479.2023.00019 (DOI)979-8-3503-4235-2 (ISBN)979-8-3503-4236-9 (ISBN)
Konferanse
2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Durres, Albania, 06-08 September 2023
Tilgjengelig fra: 2024-01-09 Laget: 2024-01-09 Sist oppdatert: 2024-01-09bibliografisk kontrollert
Prosjekter
Accelerating Digitalization Through Data: Towards Digitally Enhanced and Digital Products and ServicesStrategic Ecosystem-Driven R&D Management; Malmö universitet
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-7700-1816