The evolution of Distributed Denial-of-Service (DDoS) attack techniques on the Internet of Things (IoT) domain presents ongoing challenges as attackers increasingly emulate legitimate traffic patterns. This necessitates the continual adaptation of deep learning-based anomaly detection systems. Furthermore, the high cost of recurrently retraining deep learning models from scratch highlights the demand for adaptive detection approaches that can respond effectively to shifting threats in IoT environments. This paper investigates a range of Federated Continual Learning (FCL) techniques for identifying DDoS attacks within IoT systems, utilizing diverse federated learning approaches such as Prioritized Experience Replay (PER), Learning Without Forgetting (LWF), and Clustered Federated Learning (CFL). Continuous learning techniques, including Elastic Weight Consolidation (EWC), Agnostic Model Update (AMU), and Federated Proximal (FedProx), are also applied. The effectiveness of these methods is assessed across configurations with 16, 32, and 64 clients. Results indicate that LWF performed optimally in smaller client configurations, especially with FedAvg and FedProx, while EWC was more effective in larger setups. FedAvg and FedProx were consistently reliable strategies, whereas AMU and CFL demonstrated variable performance. This study highlights the critical role of advanced machine learning techniques in enabling real-time DDoS detection for IoT applications.

In this paper, we propose a federated continual learning-based IoT system for real-time monitoring of surface water quality, named FCL-IWQMS. This system improves surface water monitoring management by integrating sensor networks and predictive analytics, addressing the challenges of climate change and urbanization. FCL-IWQMS enables collaboration by allowing Internet of Things (IoT) devices to send only updates from their local models to a central server, consolidating them to generate an improved prediction model. This approach ensures data privacy while enhancing the accuracy of water quality predictions. The framework utilizes methods such as Prioritized Experience Replay (PER), Learning Without Forgetting (LWF), and Elastic Weight Consolidation (EWC) to refine local models in response to new data. Local updates are periodically sent to aggregation nodes, where techniques like Federated Averaging (FedAvg), Federated Trimmed Mean (FedTM), and Agnostic Model Update (AMU) are applied to consolidate updates. Evaluations with 40 clients using publicly available datasets show that the FedAvg-PER model outperforms others in predicting dissolved oxygen (DO). At the same time, AMU-LWF excels in pH predictions, and FedTM-PER leads in electrical conductivity (EC).

In the realm of IoT platforms, susceptibility to cyber-attacks is a pressing concern, necessitating the deployment of Intrusion Detection Systems (IDS). Constructing a scalable, accurate, and lightweight model without compromising data privacy poses a formidable challenge. This study assesses classical and novel approaches employing federated learning (FL) to train IDS models. Optimization through Knowledge Distillation (KD) techniques aims to enhance computational efficiency. Experimental results reveal the efficacy of federated learning, achieving an 84.5% accuracy for 15 attack types, and an impressive performance for binary network attack classification. Notably, these models exhibit shorter inference times compared to cutting-edge machine learning models trained on the Edge-IIoTset dataset, offering promising advancements in IoT security.

In the past decades, the use of urban rivers for recreational and sporting activities has gained increasing interest. However, bathing in urban surface waters is not without health risks due to short-term pollution of fecal origin, which may have an important impact on the overall population health within a region where bathing in water streams is possible. Therefore, EU member states are required to lower the contamination risk of such areas through active water quality management, as defined by the Bathing Water directory (BWD, 2006/7/EC). This paper develops and evaluates a cost-effective IoT-based water quality monitoring system, based on low-cost water quality sensors coupled with machine-learning approaches. By monitoring spatiotemporal dynamics of several physical and chemical parameters correlated with bacterial indicators, managers can more easily decide if the water quality of a bathing site is enough for usage. To determine the water suitability at particular river sites, the system employs a convolutional neural network (CNN) deep learning classifier, integrating federated learning (FL) with knowledge distillation (FedKD) to streamline model architecture, reduce communication costs, and preserve data privacy. The system is tested on the Seine and the Marne rivers (Paris area, France) and results demonstrate that FedKD outperforms centralized knowledge distillation (KD) and FL algorithms such as FedAvg and UFedAVG. Using the current features, it achieves a satisfactory average accuracy of 90.74% at Marne station.

Smart farming allows to analyze the growth of plants and to influence the parameters of our system in real time in order to optimize plant growth and support the farmer in his activity. Internet of Things (IoT) arrangements, based on the application particular sensors data measurements and intelligent processing, are bridging the holes between the cyber and physical worlds. In this paper, we propose the design and the experiment of a smart farming system based on an intelligent platform which enables prediction capabilities using artificial intelligence (AI) techniques. This system is based on the technology of wireless sensor networks and its implementation requires three main phases, i) data collection phase using sensors deployed in an agricultural field, ii) data cleaning and storage phase, and iii) predictive processing using some AI methods.