In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key. 

Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced privacy by eliminating data centralization and brings learning directly to the edge of the user’s device. Nevertheless, new privacy issues have been raised particularly during training and the exchange of parameters between servers and clients. While several privacy-preserving FL solutions have been developed to mitigate potential breaches in FL architectures, their integration poses its own set of challenges. Incorporating these privacy-preserving mechanisms into FL at the edge computing level can increase both communication and computational overheads, which may, in turn, compromise data utility and learning performance metrics. This paper provides a systematic literature review on essential methods and metrics to support the most appropriate trade-offs between FL privacy and other performance-related application requirements such as accuracy, loss, convergence time, utility, communication, and computation overhead. We aim to provide an extensive overview of recent privacy-preserving mechanisms in FL used across various applications, placing a particular focus on quantitative privacy assessment approaches in FL and the necessity of achieving a balance between privacy and the other requirements of real-world FL applications. This review collects, classifies, and discusses relevant papers in a structured manner, emphasizing challenges, open issues, and promising research directions.

A critical enabler of autonomous construction equipment is object detection, a computer vision task integral to navigation, task execution, and safety. However, challenging conditions at construction sites, such as mud splashes, dirt, and vibrations, can degrade object detection performance by causing sensor occlusions and image blurriness. Traditional adversarial training methods, which enhance model robustness by using perturbed data, are limited in construction environments due to the scarcity of diverse real-world adversarial data and the dynamic nature of these sites. Additionally, privacy concerns and site-specific data variability hinder data sharing across different construction sites. To overcome these challenges, this paper explores federated learning as a solution to enhance the robustness and adaptability of object detection models while preserving data privacy. FL enables continuous online learning without direct data exchange, offering a scalable and privacy-preserving approach to training models across diverse construction environments. Experimental results demonstrate that our approach improves model performance on the ConstScene dataset by up to ≈ 4.4% compared to the centralized AI model for object detection. 

Anomaly detection is essential for ensuring the safe and efficient operation of industrial systems like smart grids. Smart grid stations handle sensitive data and are often hesitant to share it with third-party servers for centralized anomaly detection. Federated Learning (FL) offers a viable solution to this issue by enhancing anomaly detection in smart grids without compromising data privacy. We present a method for developing an unsupervised anomaly detection system using FL applied to a synthetic dataset that mimics a real-world smart grid system’s behavior. We focus on utilizing FL’s long short-term memory autoencoder in short, LSTM-AE for anomaly detection. However, there are concerns about potential privacy breaches in the FL system. Hence, to address this issue, we propose to integrate differential privacy (DP) with FL for anomaly detection by adding artificial noise to parameters at the client side before aggregation. This method ensures data privacy while maintaining the convergence of federated learning algorithms. Moreover, this research determines the optimal privacy level to balance noise scale and model accuracy. Our findings suggest a criterion for selecting the right privacy budget of DP based on the requirement of the system to provide good level of privacy in the system while maintaining the f1-score of FL-based anomaly detection system greater than 90%.

Context: Detecting fatigue in drivers has become increasingly important for safe driving, especially with the use of more smart devices and Internet-connected vehicles. While sharing data between vehicles can enhance fatigue detection systems, privacy concerns pose significant barriers to this sharing process. We propose a Federated Learning (FL) approach for monitoring fatigue-driven behavior to address these challenges. However, there is a concern that the drivers’ private information might be leaked in the FL system. In this paper, we introduce PRIV-DRIVE, a novel approach for privacy-enhanced fatigue detection applications. Our method integrates Paillier homo-morphic encryption (PHE) with a top-k parameter selection technique, bolstering privacy and confidentiality in federated fatigue detection systems. This approach reduces communication and computation overhead while ensuring model accuracy. To the best of our knowledge, this is the first paper to implement PHE in FL setups for fatigue detection applications. We ran several experiments and evaluated the PRIV-DRIVE method. The results show substantial efficiency gains with different HE key sizes, reducing computation time by up to 96% and communication traffic by up to 95%. Importantly, these improvements have minimal impact on accuracy, effectively meeting the requirements of fatigue detection applications. 

The IoT comprises billions of intelligent devices that interact, gather, and share data via sensors and actuators. The Industrial IoT (IIoT), specifically used in industry and production, is used in automation and rapid production of goods based on machine learning techniques. Similarly, Cyber-Physical System (CPS) plays a vital role in the next-generation industry. The CPSs are intelligent systems that interconnect the physical world through embedded systems, sensors, actuators with the cyberworld. We require a communication backbone for interconnecting and information processing, which 6G networks can fulfill. The 6G has a higher capacity and improved characteristics than previous cellular networks, accelerating the applications and deployments of 6G-based IIoT networks in industry platforms. This chapter discusses how the 6G networks can help interconnect the CPS and IIoT through smart connection, digital twinning, and immersive technology.

ABSTRACT. Anomaly detection is critical in industrial systems such as smart grid systems to guarantee their safe and effective operation. The smart grid stations contain sensitive data, and they are concerned about sharing it with a third-party server to establish a centralized anomaly detection system. Federated Learning (FL) is a feasible solution to these problems for enhancing anomaly detection in smart grid systems. This study describes a method for developing an unsupervised anomaly detection based on FL system using a synthetic dataset based on real-world grid system behavior. The paper investigates the usage of FL’s long short-term memory autoencoder (LSTM-AE) for anomaly detection. For more accurate identification, this research explores the performance of integrating LSTM-AE with one-class support vector machine (OC-SVM) and isolation forest (IF) and compares their results with a threshold-based anomaly detection approach. Moreover, an approach is described for generating synthetic anomalies with different levels of difficulty to evaluate the robustness of the anomaly detection FL model. The FL models results are compared with the centralized version of the models as a baseline and the results show that FL models outperformed the centralized approach by detecting higher outlier data by achieving 99% F1-Score.

Context: Speech Emotion Recognition (SER) is a valuable technology that identifies human emotions from spoken language, enabling the development of context-aware and personalized intelligent systems. To protect user privacy, Federated Learning (FL) has been introduced, enabling local training of models on user devices. However, FL raises concerns about the potential exposure of sensitive information from local model parameters, which is especially critical in applications like SER that involve personal voice data. Local Differential Privacy (LDP) has prevented privacy leaks in image and video data. However, it encounters notable accuracy degradation when applied to speech data, especially in the presence of high noise levels. In this paper, we propose an approach called LDP-FL with CSS, which combines LDP with a novel client selection strategy (CSS). By leveraging CSS, we aim to improve the representatives of updates and mitigate the adverse effects of noise on SER accuracy while ensuring client privacy through LDP. Furthermore, we conducted model inversion attacks to evaluate the robustness of LDP-FL in preserving privacy. These attacks involved an adversary attempting to reconstruct individuals' voice samples using the output labels provided by the SER model. The evaluation results reveal that LDP-FL with CSS achieved an accuracy of 65-70%, which is 4% lower than the initial SER model accuracy. Furthermore, LDP-FL demonstrated exceptional resilience against model inversion attacks, outperforming the non-LDP method by a factor of 10. Overall, our analysis emphasizes the importance of achieving a balance between privacy and accuracy in accordance with the requirements of the SER application.

DAIS is a step forward in the area of artificial intelligence and edge computing. DAIS intends to create a complete framework for self-organizing, energy efficient and private-by-design distributed AI. DAIS is a European project with a consortium of 47 partners from 11 countries coordinated by RISE Research Institute of Sweden.

Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device. In FL, Hyperparameters (HP) significantly affect the training overhead regarding computation and transmission time, computation and transmission load, as well as model accuracy. This paper presents a novel approach where Hyperparameters Optimization (HPO) is used to optimize the performance of the FL model for Speech Emotion Recognition (SER) application. To solve this problem, both Single-Objective Optimization (SOO) and Multi-Objective Optimization (MOO) models are developed and evaluated. The optimization model includes two objectives: accuracy and total execution time. Numerical results show that optimal Hyperparameters (HP) settings allow for improving both the accuracy of the model and its computation time. The proposed method assists FL system designers in finding optimal parameters setup, allowing them to carry out model design and development efficiently depending on their goals.