federated learning

Decentralized Learning with Federated Graph Neural Networks

This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs). This framework addresses the limitations of traditional federated learning, particularly when dealing with graph-structured data, where raw data sharing poses significant privacy concerns. The system avoids direct data exchange by allowing devices to collaboratively learn through the exchange of model updates, preserving data privacy while enabling effective knowledge transfer. The proposed FedGNN architecture consists of local graphs and locally trained GNNs, synchronized via a novel aggregation mechanism. We demonstrate the feasibility and effectiveness of this approach through a theoretical analysis and outline the key components required for implementation. The system is designed to be scalable and adaptable to various decentralized network topologies. This work represents a significant step towards privacy-preserving collaborative learning in complex, graph-based environments.

Federated learning explainable deep learning framework for enhanced internet of medical things security

The Internet of Medical Things (IoMT) enables remote patient monitoring, providing timely diagnostic assessments crucial for saving lives. Despite these benefits, IoMT systems face significant security challenges that endanger patient privacy and data integrity. Existing centralized machine learning (ML) and deep learning (DL) solutions, while useful, are “black boxes” that raise critical concerns about data privacy and ethical compliance, as sharing sensitive patient information in a centralized system may violate confidentiality, integrity, and availability, particularly in healthcare sectors where patient data is highly confidential. Motivated by these challenges, we propose a novel federated learning (FL) explainable deep recurrent neural network (DRNN)-augmented long short-term memory (LSTM) framework for anomaly intrusion in IoMT systems, named federated recurrent short-term memory. The proposed decentralized framework processes data on local devices, sharing only learned model weights with the central server to safeguard data integrity. Training the DRNN-LSTM presents a non-convex optimization problem that is computationally expensive. To address this issue, we propose a new Bayesian Optimization (BO) method, which models the objective function stochastically and intelligently selects the most promising hyperparameter configurations to identify the hyperparameter vector that maximizes the objective function. The Adam optimizer was implemented to address gradient descent resource issues and improve the speed of convergence with a learning rate of 0.001. We introduced Shapley Additive Explanations (XAI) to ensure security decisions are transparent, a key advantage over existing black-box approaches that helps meet ethical guidelines such as HIPAA and GDPR. The framework was evaluated on the Washington University in St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS), as well as the new CIC-IoMT2024 (IoMT-specific) and BoT-IoT (general IoT) benchmark datasets. The results show strong detection capabilities, with high detection rates for critical attack types such as DoS with 99.94%, DoS SYN with 100%, and DoS TCP with 100%. The model also demonstrated exceptionally high precision for classes like DoS SYN, DDoS TCP, and ARP spoofing. Our proposed federated recurrent short-term memory framework achieves a robust balance between detection fidelity and computational efficiency, making it a reliable solution for intrusion detection in resource-constrained IoMT infrastructures.

Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

Collaborative AI experimentation in industry-academia requires environments that support rapid trials while maintaining controlled access, organisational isolation, and traceable workflows. Although interest in AI sandboxes is increasing, practical guidance on designing and building governance-aware experimentation platforms remains limited. This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders. The sandbox was developed in an industry-academia ecosystem using iteratively validated requirements gathered from industrial partners. The solution adopts a layered reference architecture that separates a multi-tenant presentation layer from a backend control plane and isolates execution and data management concerns into dedicated layers. The sandbox supports governed onboarding, project-based collaboration, controlled access to AI services, and traceable experimentation through approval workflows and audit logging. By structuring experiment context and governance decisions as persistent records, the sandbox enables evaluation evidence to be reused and compared across projects and stakeholders. The development experience yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platforms.

SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING

Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data generated by modern urban environments. The integration of the Internet of Things (IoT) with cloud computing provides an effective solution for developing scalable smart city platforms capable of supporting real-time monitoring, intelligent decision-making, and efficient resource management. This paper presents a scalable cloud-enabled smart city architecture that integrates IoT sensing devices, edge gateways, cloud infrastructure, big data analytics, and artificial intelligence to support multiple smart city services within a unified platform. The proposed architecture employs a layered framework consisting of perception, communication, edge computing, cloud services, data analytics, application, and security layers to improve scalability, interoperability, reliability, and service availability. The study critically reviews recent advances in IoT cloud integration, identifies major challenges including security, privacy, latency, interoperability, and energy efficiency, and proposes practical strategies for addressing these limitations through containerization, micro services,edge cloud collaboration, and AI-driven resource orchestration. The proposed framework demonstrates how cloud computing can dynamically allocate computational resources to accommodate growing IoT deployments while maintaining quality of service. The paper concludes that scalable IoT-cloud platforms represent the foundation for next-generation smart cities and recommends future integration with digital twins, federated learning, block chain, and 6G communication technologies for improved sustainability and resilience.

SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING

Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data generated by modern urban environments. The integration of the Internet of Things (IoT) with cloud computing provides an effective solution for developing scalable smart city platforms capable of supporting real-time monitoring, intelligent decision-making, and efficient resource management. This paper presents a scalable cloud-enabled smart city architecture that integrates IoT sensing devices, edge gateways, cloud infrastructure, big data analytics, and artificial intelligence to support multiple smart city services within a unified platform. The proposed architecture employs a layered framework consisting of perception, communication, edge computing, cloud services, data analytics, application, and security layers to improve scalability, interoperability, reliability, and service availability. The study critically reviews recent advances in IoT cloud integration, identifies major challenges including security, privacy, latency, interoperability, and energy efficiency, and proposes practical strategies for addressing these limitations through containerization, micro services,edge cloud collaboration, and AI-driven resource orchestration. The proposed framework demonstrates how cloud computing can dynamically allocate computational resources to accommodate growing IoT deployments while maintaining quality of service. The paper concludes that scalable IoT-cloud platforms represent the foundation for next-generation smart cities and recommends future integration with digital twins, federated learning, block chain, and 6G communication technologies for improved sustainability and resilience.

Learning from agile software development

Self-organized Learning in Software Factory: Experiences and Lessons Learned

What Can Software Startuppers Learn from the Artistic Design Flow? Experiences, Reflections and Future Avenues

Why Early-Stage Software Startups Fail: A Behavioral Framework

Software startups are newly created companies with little operating history and oriented towards producing cutting-edge products. As their time and resources are extremely scarce, and one failed project can put them out of business, startups need effective practices to face with those unique challenges. However, only few scientific studies attempt to address characteristics of failure, especially during the early-stage. With this study we aim to raise our understanding of the failure of early-stage software startup companies. This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach. The results present how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework. Despite strategies reveal the first need to understand the problem/solution fit, actual executions prioritize the development of the product to launch on the market as quickly as possible to verify product/market fit, neglecting the necessary learning process.

Software development in startup companies: A systematic mapping study

Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Towards Abstraction and Automation in Software Engineering

Novel software development approaches are embracing abstraction and automation techniques. It is claimed that abstraction and automation techniques increase the productivity, improve the reusability and lower the complexity of the projects. In this study we address these new frontiers of software development by investigating on one novel proposal, namely the Ball. The Ball is an information ecosystem for authorised information containing web content, digital content as well as service development and integration. It is claimed to improve the reusability, productivity and security of software development while lowering the complexity. While improving the software developer’s productivity it should produce smaller and more reasonable software systems, leading to a better reusability and a shorter learning phase for new developers. Up to now there exists no evidence to support these claims. In this study we analyse the Ball ecosystem from multiple perspectives. We compare it to related approaches in order to find its advantages and disadvantages. In order to provide empirical data we replicated a study where a mobile information system was developed using three different technologies. The results of this study show that the Ball ecosystem has the potential to improve the productivity of software development. However, it

A Simultaneous, Multidisciplinary Development and Design Journey - Reflections on Prototyping

This paper proposes a wayfaring approach for the early concept creation stage of development projects that have a very high degree of intended innovation and thus uncertainty. The method is supported by a concrete game design example involving the development of a tangible programming interface for virtual car racing games. We focus onto projects that not only have high degrees of freedom, for example in terms of reframing the problem or iterating the final project vision, but are also complex in nature. For example, these can be projects that allow for the exploration and exploitation of unknown unknowns and serendipity findings. Process wise we are primarily focusing onto the early stage that precedes the requirement fixation, which we see as more dynamic and evolutionary in nature. The core conceptual elements that we have derived from the development experiences are: simultaneous prototyping in multiple disciplines (such as computer science, electronics and mechanics and engineering in general, abductive learning based on the outcome of rapid cycles of designing, building and testing prototypes (probing), and the importance of includingall the involved disciplines (knowledge domains) from the beginning of the project on.