6,124 research outputs found
Multi Agent Systems in Logistics: A Literature and State-of-the-art Review
Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Blockchain-Enabled Federated Learning Approach for Vehicular Networks
Data from interconnected vehicles may contain sensitive information such as
location, driving behavior, personal identifiers, etc. Without adequate
safeguards, sharing this data jeopardizes data privacy and system security. The
current centralized data-sharing paradigm in these systems raises particular
concerns about data privacy. Recognizing these challenges, the shift towards
decentralized interactions in technology, as echoed by the principles of
Industry 5.0, becomes paramount. This work is closely aligned with these
principles, emphasizing decentralized, human-centric, and secure technological
interactions in an interconnected vehicular ecosystem. To embody this, we
propose a practical approach that merges two emerging technologies: Federated
Learning (FL) and Blockchain. The integration of these technologies enables the
creation of a decentralized vehicular network. In this setting, vehicles can
learn from each other without compromising privacy while also ensuring data
integrity and accountability. Initial experiments show that compared to
conventional decentralized federated learning techniques, our proposed approach
significantly enhances the performance and security of vehicular networks. The
system's accuracy stands at 91.92\%. While this may appear to be low in
comparison to state-of-the-art federated learning models, our work is
noteworthy because, unlike others, it was achieved in a malicious vehicle
setting. Despite the challenging environment, our method maintains high
accuracy, making it a competent solution for preserving data privacy in
vehicular networks.Comment: 7 page
Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges
In the last decade, Federated Learning (FL) has gained relevance in training
collaborative models without sharing sensitive data. Since its birth,
Centralized FL (CFL) has been the most common approach in the literature, where
a central entity creates a global model. However, a centralized approach leads
to increased latency due to bottlenecks, heightened vulnerability to system
failures, and trustworthiness concerns affecting the entity responsible for the
global model creation. Decentralized Federated Learning (DFL) emerged to
address these concerns by promoting decentralized model aggregation and
minimizing reliance on centralized architectures. However, despite the work
done in DFL, the literature has not (i) studied the main aspects
differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and
evaluate new solutions; and (iii) reviewed application scenarios using DFL.
Thus, this article identifies and analyzes the main fundamentals of DFL in
terms of federation architectures, topologies, communication mechanisms,
security approaches, and key performance indicators. Additionally, the paper at
hand explores existing mechanisms to optimize critical DFL fundamentals. Then,
the most relevant features of the current DFL frameworks are reviewed and
compared. After that, it analyzes the most used DFL application scenarios,
identifying solutions based on the fundamentals and frameworks previously
defined. Finally, the evolution of existing DFL solutions is studied to provide
a list of trends, lessons learned, and open challenges
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