In today's interconnected cyber landscape, Distributed Denial of Service (DDoS) attacks represent a significant threat to the smooth functioning of online infrastructures. The nature of DDoS attacks, characterized by their distributed and dynamic nature, poses significant challenges for traditional centralized approaches to model training; however, the challenges of collaborative DDoS detection are compounded by stringent data privacy regulations, leaving mitigation efforts largely reliant on standalone and inflexible firewalls. Federated Learning (FL) represents a cutting-edge innovation in cybersecurity, presenting a revolutionary method for collectively training deep learning models without compromising sensitive data. Despite its promise, practical hurdles remain, particularly the reliance of most FL algorithms on centralized, server-side data for model evalu-ation-though some approaches avoid this centralized testing dependency. This limitation hinders the applicability of FL, especially in scenarios involving zero-day attacks on clients. Our paper examines a key hypothesis: whether the aggregated information from multiple clients can be effectively utilized to develop a global model that is inherently more resilient to zeroday attacks compared to models trained solely on individual client data. To investigate this, we introduce a methodology wherein FL models are trained on established DDoS attacks and subsequently evaluated against entirely novel, unencountered attacks, simulating zero-day scenarios at the client level. To ensure that each client contributes effectively to the training process, we utilize Jensen-Shannon Divergence (JSD) to evaluate and filter client updates based on their alignment with the global model. Building on this, we implement a kernel density estimation-based aggregation method to effectively mitigate feature distribution bias-a common issue in DDoS detection within FL environments. This approach forms a core component of our proposed framework, FLAME, which is built using the distributed framework Flower to realistically simulate FL in a decentralized setting. The code for our implementation can be found at: https://github.com/MODAL-UNINA/FLAME.

Efficient and accurate detection of plant leaf diseases is essential for protecting crop health and promoting sustainable and precision agriculture practices. However, the decentralized nature of agricultural data, combined with the inherent limitations of centralized Machine Learning (ML), presents significant challenges for developing scalable, privacy-preserving solutions. In this paper, we introduce AGRIFOLD, a Federated Learning (FL) framework designed to enable collaborative training of a lightweight Convolutional Neural Network (CNN) across diverse and distributed datasets while maintaining data privacy. By integrating an Efficient Channel Attention (ECA) mechanism into the VGG16 architecture, AGRIFOLD significantly improves classification accuracy and enhances interpretability through heatmaps that highlight regions affected by diseases. We evaluate the FL model using various aggregation methods, including FedAvg, FedProx, SCAFFOLD, FedBN, and FedDF, obtaining good accuracy levels for all tested aggregation strategies, with SCAFFOLD achieving the best overall performance. The model’s lightweight design, optimized through ablation and pruning techniques, facilitates deployment on resource-constrained edge devices. Additionally, to further support farmers’ decision-making, the framework incorporates a natural language processing-based recommender system that provides tailored treatment suggestions. Comprehensive experiments conducted on 12 heterogeneous datasets demonstrate high classification accuracy across 9 distinct leaf disease classes and healthy leaves, underscoring the practical potential of FL-based solutions for sustainable, real-world agricultural applications. The AGRIFOLD source code is available at https://github.com/MODAL-UNINA/AGRIFOLD .

AgentAI represents a transformative approach within distributed Artificial Intelligence (AI) in which autonomous agents work either individually or collaboratively in decentralized environments to address challenging problems. AgentAI enhances scalability, robustness, and flexibility by utilizing advanced communication, learning, and decision-making capabilities, making it integral to diverse applications in Industry 4.0. The ability of AI systems to interpret sensory data in open-world environments has seen significant advancements in recent years. This progress emphasizes the need to move beyond reductionist approaches and embrace more embodied and cohesive systems, which integrate foundational models into agent-driven actions. Existing surveys often focus on isolated domains or specific autonomy levels, lacking a cohesive analysis that spans the full spectrum of AgentAI development in Industry 4.0. This survey explicitly fills this gap by introducing a multi-domain taxonomy and by systematically analyzing both non-autonomous and fully autonomous AgentAI systems, offering a comprehensive synthesis not previously available in the literature. Additionally, the paper extends the discussion to Industry 5.0 and 6.0, exploring the evolution of AgentAI from automation to collaboration and, ultimately, to fully autonomous systems. This comprehensive analysis highlights the potential of AgentAI in driving industries toward a more efficient, sustainable, and adaptable future.

The paradigm of Augmented Intelligence of Things (AIoT) aims to empower Internet of Things (IoT) devices with intelligent capabilities to analyze data, make informed decisions, and execute actions autonomously. This study focuses on enhancing collaboration between vehicles and road infrastructure within the AIoT framework, particularly in the context of self-driving cars and smart city environments. A Proof of Concept (PoC) is presented, introducing a vehicle road cooperation framework tailored for online-vehicle-infrastructure cooperation (VIC) forecasting tasks. This framework enables real-time information exchange and trajectory prediction of target agents by leveraging IoT sensor technologies and incorporating two layers of cooperation: 1) ego-vehicles and 2) infrastructures. Experimental results demonstrate that the integration of information from both layers enhances prediction metrics compared to approaches focusing on individual layers. Comparative analysis with existing method, PP-VIC, underscores the superiority of the proposed framework in trajectory prediction. This research offers a promising avenue for enhancing communication and collaboration between infrastructure and autonomous vehicles, thereby contributing to the development of more efficient and safer transportation systems in smart cities.

Deep learning (DL) has significantly advanced Industry 4.0 by leveraging data from the Industrial Internet of Things (IIoT) to enable smart manufacturing, predictive maintenance, and data-driven product marketing. However, multimodal industrial data presents challenges for traditional frameworks, including scalability, data privacy, and integration efficiency. This paper introduces an efficient product retrieval framework for e-commerce systems, addressing privacy and performance challenges through federated learning (FL). Specifically, we propose FLAIR (Federated Learning for Augmented Industrial Retrieval), a novel part retrieval system where distributed warehouses collaboratively train a multimodal foundation model, CLIP (Contrastive Language-Image Pre-Training), by fine-tuning only the Adapter module via FL, ensuring data privacy and efficiency. To address the limited availability of multimodal industrial data, our framework incorporates effective data augmentation strategies to enhance the diversity and quality of the training dataset. Comprehensive experiments on the Industrial Language-Image Dataset (ILID) highlight that FLAIR holds effective privacy safeguards and strong retrieval capabilities. Additionally, an advanced e-commerce recommendation system built on FLAIR showcases its practical effectiveness. FLAIR represents the first application of FL for industrial product retrieval, optimizing part searches, inventory management, and customer experience while maintaining data security. The complete code is available at https://github.com/MODAL-UNINA/FLAIR.

Generative artificial intelligence (GenAI) and digital twin (DT) technologies have individually demonstrated valuable capabilities across a range of fields. Their integration, however, offers a unique synergy with the potential to bring meaningful advancements in various sectors. In this survey, we explore the fusion of GenAI and DT, highlighting their combined ability to enhance insights, optimizations, and innovative solutions. We begin by clarifying the core principles of each technology and then outline their collaborative applications. Furthermore, we provide a detailed taxonomy of areas where GenAI has been leveraged within the realm of DT, and conversely, where DT technology has been enhanced through GenAI techniques. By systematically categorizing these applications, we aim to offer a clear perspective on the interplay between GenAI and DT across different sectors.

Federated learning (FL) is a distributed machine learning (ML) approach that enables models to be trained on client devices while ensuring the privacy of user data. Model aggregation, also known as model fusion, plays a vital role in FL. It involves combining locally generated models from client devices into a single global model while maintaining user data privacy. However, the accuracy and reliability of the resulting global model depend on the aggregation method chosen, making the selection of an appropriate method crucial. Initially, the simple averaging of model weights was the most commonly used method. However, due to its limitations in handling low-quality or malicious models, alternative techniques have been explored. As FL gains popularity in various domains, it is crucial to have a comprehensive understanding of the available model aggregation techniques and their respective strengths and limitations. However, there is currently a significant gap in the literature when it comes to systematic and comprehensive reviews of these techniques. To address this gap, this paper presents a systematic literature review encompassing 201 studies on model aggregation in FL. The focus is on summarizing the proposed techniques and the ones currently applied for model fusion. This survey serves as a valuable resource for researchers to enhance and develop new aggregation techniques, as well as for practitioners to select the most appropriate method for their FL applications.

Environment perception through deep computation in unstructured environments is important for the construction of autonomous navigation systems. Most research focuses on navigation in structured scenes, including indoor mobility and driving along roads, while neglecting to consider unstructured environments, which often contain diverse heights and distributions. In addition, existing depth estimation algorithms based on deep learning often need to complete training under the supervision of Ground truth, and GT data with a large number of labels are not always easy to obtain. To tackle this issue, this paper proposes an unsupervised stereo depth estimation method for processing UAV navigation images in an unstructured environment. The method contains a primitive U-shaped CNN network architecture for processing such scenes. The feature extraction layer of the network is based on the YOLOv3 residual structure, and additional attention modules help the network enhance its ability to perceive image features. Finally, depth estimation experiments on the unstructured environments dataset Mid-Air further demonstrate the effectiveness and reliability of the proposed method.

Crack is one of the critical factors that degrade the performance of machinery manufacturing equipment. Recently, physics-informed neural networks (PINNs) have received attention due to their strong potential in solving physical problems. For fracture problems, PINNs have been used to predict crack paths by minimizing the variational energy of discrete domains where refined meshes are necessary. To obtain refined meshes, posteriori adaptive refinement techniques are commonly used to perform local refinement of the mesh based on errors in the intermediate calculation process; thus, they require pretest calculations. However, it is computationally expensive to precalculate complex problems, especially crack propagation. To solve this problem, we propose a PointNet-based adaptive refinement method to avoid precalculation when constructing the discrete domain. The proposed method is applied to simulate crack propagation using a PINN. Results show that the proposed method can be used to obtain reliable results efficiently when using the PINN framework.

Geologic hazards (geohazards) are naturally occurring or human-activity-induced geologic conditions capable of causing damage or loss of property and/or life. geohazards, such as landslides, surface subsidence, and earthquakes, can seriously affect and threaten life, property, or public safety. geohazards prevention is the application of geologic engineering principles and existing and emerging technologies to reduce, minimize, or prevent the effects of various geologic hazards. Monitoring and early warning are the most common strategies for geohazards prevention. With the development of the Internet of Things (IoT), an emerging new idea is to apply IoT technology to enhance the accuracy and efficiency of monitoring and early warning systems for geohazards prevention. This article aims to present a comprehensive survey of relevant research and technological developments of the IoT applied in geohazards prevention. It first surveys the applications of the IoT in the monitoring and early warning of seven types of common geohazards, including landslides, debris flow, rockfall, surface subsidence, surface collapse, surface cracks, and earthquakes, then investigates the key technologies in geohazards prevention when utilizing the IoT, and finally summarizes the challenges in IoT-based monitoring and early warning systems for geohazards prevention. Moreover, this article also highlights the future directions for employing the IoT for geohazards prevention.