This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which in turn makes IoT networks an open-source of Personally Identifiable Information data for malicious attackers to steal, manipulate and perform nefarious activities. Huge interest has developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data is widely available and transferable to the main server because data is born at the edge and is generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose federated learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on Gated Recurrent Units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of the FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

Although federated learning offers a level of privacy by aggregating user data without direct access, it remains inherently vulnerable to various attacks, including poisoning attacks where malicious actors submit gradients that reduce model accuracy. In addressing model poisoning attacks, existing defense strategies primarily concentrate on detecting suspicious local gradients over plaintext. However, detecting non-independent and identically distributed encrypted gradients poses significant challenges for existing methods. Moreover, tackling computational complexity and communication overhead becomes crucial in privacy-preserving federated learning, particularly in the context of encrypted gradients. To address these concerns, we propose a robust privacy-preserving federated learning model resilient against model poisoning attacks without sacrificing accuracy. Our approach introduces an internal auditor that evaluates encrypted gradient similarity and distribution to differentiate between benign and malicious gradients, employing a Gaussian Mixture Model and Mahalanobis Distance for byzantine-tolerant aggregation. The proposed model utilizes Additive Homomorphic Encryption to ensure confidentiality while minimizing computational and communication overhead. Our model demonstrates superior performance in accuracy and privacy compared to existing strategies and encryption techniques, such as Fully Homomorphic Encryption and Two-Trapdoor Homomorphic Encryption. The proposed model effectively addresses the challenge of detecting maliciously encrypted non-independent and identically distributed gradients with low computational and communication overhead.

Smart grid technology increases reliability, security and efficiency of the electrical grids. However, its strong dependencies on digital communication technology brings up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. Proposed method applies feature extraction utilizing Symbolic Dynamic Filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. Simulation results on IEEE 39, 118 and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98% and false positive rate of less than 2%.

The potential of blockchain has been extensively discussed in the literature and media mainly in finance and payment industry. One relatively recent trend is at the enterprise-level, where blockchain serves as the infrastructure for internet security and immutability. Emerging application domains include Industry 4.0 and Industrial Internet of Things (IIoT). Therefore, in this paper, we comprehensively review existing blockchain applications in Industry 4.0 and IIoT settings. Specifically, we present the current research trends in each of the related industrial sectors, as well as successful commercial implementations of blockchain in these relevant sectors. We also discuss industry-specific challenges for the implementation of blockchain in each sector. Further, we present currently open issues in the adoption of the blockchain technology in Industry 4.0 and discuss newer application areas. We hope that our findings pave the way for empowering and facilitating research in this domain, and assist decision-makers in their blockchain adoption and investment in Industry 4.0 and IIoT space.