D4.2 - Distributed Network Design

The rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) are driving 
innovation across various domains, from autonomous systems to personalized medicine. However, 
achieving the full potential of these technologies requires robust research infrastructures capable 
of supporting distributed, scalable, and secure AI development. dAIEDGE aims to establish a cutting
edge virtual research lab that addresses these challenges by enabling seamless collaboration, 
resource sharing, and AI model development across geographically distributed environments. 
In this context, Task T4.2 focuses on designing a distributed network for the dAIEDGE virtual 
research lab, leveraging blockchain technology as a core enabler for decentralization, security, and 
trust. This task aims to establish the architecture and technical foundation needed to interconnect 
diverse research infrastructures, facilitate remote access to resources, and create a secure and 
scalable environment for AI research and development. 
Objectives of Task T4.2 
The primary objective of Task T4.2 is to design a blockchain-based distributed network for the virtual 
research lab. This design process encompasses the identification and specification of functional and 
non-functional requirements, the development of a formal architecture model, and the definition 
of an Open API that will enable seamless interconnection and aggregation of remote research 
assets. 
Specific objectives include: 
• Designing a Blockchain-Enabled Research Network: Leverage blockchain to provide 
decentralized coordination, secure interactions, and transparent access control across the 
distributed infrastructure. 
• Defining Functional and Non-Functional Requirements: Establish clear requirements that 
will guide the design of the architecture and Open API, ensuring scalability, interoperability, 
security, and performance. 
• Developing a Formal Architecture Model: Propose an architecture that integrates the 
blockchain network with orchestration solutions (e.g., Kubernetes) to optimize AI 
deployment, provisioning, and resource management. 
• Collaborating Across Stakeholders: Engage with key partners to align the design with 
existing platforms such as the Bonseyes Marketplace, the AIoD platform roadmap, and other 
research infrastructures.

Expanding the Design via Use Case Implementation in T4.4 
The design and architecture model developed in Task T4.2 will be further expanded and refined 
through the implementation of specific use cases in Task T4.4. This iterative approach will involve 
close collaboration with project partners, who will implement and validate the architecture in real
world scenarios. Through this process, the Formal Architecture Model and Functional and Non
Functional Requirements will be continually developed and enhanced, informed by practical 
feedback and insights gathered during these implementations. An overview of the three use cases 
are below: 
Use Case 1 : Benchmarks: Enhancing HW/SW Benchmarking for AI at the Edge 
This use case leverages and extends the benchmarking suite developed in the Bonseyes 
project, focusing on evaluating AI applications on edge and deep edge devices. The 
benchmarking process includes assessing performance, power efficiency, and scalability 
across a variety of hardware/software configurations, ranging from low-power 
microcontrollers to more capable edge processors. The objectives are to: 
o Validate AI Workflows on Diverse Edge Devices: Measure how well AI models 
perform when deployed on various edge platforms, considering both compute
constrained devices and more capable edge infrastructure. 
o Optimize Hardware/Software Co-Design: Generate insights that guide the co
optimization of AI models, software frameworks, and edge hardware configurations, 
ensuring efficient deployment. 
o Drive Standardization: Provide benchmarks that contribute to industry-standard 
metrics for evaluating AI at the edge, facilitating wider adoption of best practices. 
This use case is crucial for refining the virtual research lab’s capability to support edge AI 
deployment, ensuring that the architecture is robust, scalable, and adaptable to different 
hardware environments. 
Use Case 2 : Protection of User Assets: Secure AI Asset Management 
In this use case, the focus is on testing a framework that integrates encryption mechanisms 
for safeguarding sensitive AI Assets, such as models, datasets, and proprietary algorithms 
designed to establish trust and integrity within the virtual research lab. The framework 
includes: 
• Keyless Code Signing: Implementing secure code-signing mechanisms that allow AI 
assets, including models and software components, to be verified without relying on 
traditional key-based systems. This approach reduces complexity and enhances 
scalability in distributed environments. 
• Attestations and Provenance: Introducing mechanisms for securely recording and 
verifying the origin and integrity of AI assets. This includes tracking the history of data, 

models, and algorithms as they are developed, shared, and deployed across the lab, 
ensuring traceability and accountability. 
• Secure Data Handling: Integrating security protocols that protect sensitive AI assets and 
data throughout their lifecycle, ensuring compliance with privacy regulations and 
minimizing the risk of unauthorized access. 
This use case aligns directly with the security requirements guiding the architecture, 
ensuring that the virtual lab supports trusted, secure collaboration among stakeholders. 
Use Case 3 : Experimentation, Industrialization, and Interoperability 
This use case focuses on bridging the gap between research prototypes and real-world edge 
applications by enabling the transition of AI assets from experimental stages to industrial 
deployment. The key activities include: 
• AI Asset Integration and Deployment: Providing a pipeline for taking AI models and 
solutions developed within the virtual lab and deploying them on edge devices, with a 
particular focus on platforms based on RISC-V architecture. 
• Interoperability and Platform Integration: Ensuring that AI Assets are interoperable 
across different edge computing environments and platforms. This involves close 
collaboration with relevant AI platforms to achieve seamless integration. 
• Industrialization of AI Workflows: Supporting the move from experimental setups to 
scalable, production-ready systems, including the automation of deployment, 
monitoring, and updates. 
Adopting a use case-driven approach to design and validation offers several key advantages: 
1. Real-World Validation: By grounding the design in concrete use cases, the architecture 
model and requirements are more likely to address real-world needs, ensuring the virtual 
lab is relevant, practical, and aligned with industry demands. 
2. Iterative Refinement: Use case implementations allow for an iterative design process where 
feedback loops from real deployments can be integrated back into the architecture. This 
leads to continuous improvement and ensures that the final solution is both robust and 
flexible. 
3. Comprehensive Requirement Gathering: Use cases help in capturing both functional and 
non-functional requirements that may not be fully evident in a purely theoretical design. For 
example, performance bottlenecks, security issues, and deployment challenges can be 
identified and resolved through practical testing. 
4. Cross-Domain Applicability: The diverse nature of the use cases (from AI benchmarking to 
secure code signing and encryption) ensures that the virtual research lab’s design is versatile 
and adaptable across multiple domains and application scenarios. 
5. Stakeholder Engagement: Involving project partners in the implementation and testing 
phases encourages active engagement and collaboration, leading to a more aligned and

consensus-driven outcome. This also helps in leveraging partner expertise in areas like 
orchestration, security, and edge computing. 
Through the implementation of these use cases in Task T4.4, the design of the dAIEDGE virtual 
research lab will be refined to target a TRL level of 3-4. 
Scope and Structure of the Deliverable 
This deliverable presents the results of Task T4.2, detailing the design and specification of the virtual 
research lab. The report is structured as follows: 
• Section 1: Requirement Analysis: Provides a detailed analysis of the functional and non
functional requirements, including security, scalability, and interoperability considerations. 
• Section 2: Architecture Design: Describes the proposed architecture model for the 
blockchain-enabled distributed network, highlighting the integration of key components and 
services. 
• Section 3: Open API Specification: Outlines the design and functionalities of the Open API, 
detailing the endpoints, access controls, and integration with edge components. 
• Section 4: Use Cases: Provides an overview of the specific use cases that guided the design 
and validation of the architecture. 
• Section 5: Conclusions and Next Steps: Summarizes the key outcomes and provides an 
outlook on further developments, including alignment with Task 4.4. 
The design and specifications presented in this report aim to lay a strong foundation for the 
development and deployment of the dAIEDGE virtual research lab, ultimately advancing the state
of-the-art in distributed AI research infrastructures. 
Stakeholder Contributions: 
• BCA (Task Leader): Leading the requirements and architecture for the VLab, leveraging 
expertise from the Bonseyes Marketplace and aligning with the AIoD (AI on Demand) 
platform roadmap. 
• BTH: Contributing orchestration concepts based on Kubernetes for optimizing ML 
deployment. 
• CETIC: Identifying integration requirements with edge-focused virtual labs and 
cybersecurity-focused software factories. 
• KUL: Focusing on security requirements and employing security- and privacy-by-design 
principles. 
• HES-SO: Expertise on heterogeneous edge systems and dedicated HPC infrastructure. 
• SED: Contribution to Open API for provisioning and autosizing services using AI and 
optimization techniques. 
• USAL: Contributing to blockchain API design, remote asset access, and eliciting/analyzing 
functional requirements.