HydroFedNet: An Intent-Based Unified Federated Framework for Multisource Water Quality Monitoring

Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation

Artificial intelligence has significantly advanced recently, penetrating various sectors such as healthcare, finance, and smart technology. A key factor driving this progress is federated learning (FL), an innovative decentralized machine learning approach. FL facilitates training models on diverse datasets without exchanging raw data, promoting collaboration while addressing privacy concerns associated with centralized data repositories. The framework utilizes a global deep-learning model from the central server. This study analyzes recent advancements and identifies several unanswered questions stemming from the expansion of FL research. It delves into privacy issues in the FL landscape, including secure multi-party computation, homomorphic encryption, differential privacy, and stochastic gradient descent. Additionally, it explores the application of artificial intelligence in public education, encompassing adaptive technologies, predictive analytics, and edge computing. Furthermore, it investigates the potential of FL in training models with low-power constraints, such as those in the Internet of Things (IoT). Nevertheless, challenges like communication overhead, interoperability standards, and data security concerns persist. This study underscores the transformative potential of federated learning in shaping the future of AI and emphasizes its crucial role in preserving privacy, enabling collaborative learning, and efficient learning mechanisms. Federated learning has been deployed in various domains, including wireless communications, service delivery, smart personal systems, and healthcare. This chapter charts a course forward by examining current FL challenges such as privacy preservation, communication overhead, low performance, and efforts to establish robust models. Federated learning represents a promising paradigm that aims to revolutionize artificial intelligence by facilitating privacy-preserving collaboration and enabling decentralized learning systems. While it holds significant potential, it also confronts challenges that require attention.

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising AI solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate TCN (Temporal Convolutional Network), GCN (Graph Convolutional Neural Network) and GRU (Gated Recurrent Unit) architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

This research addresses the importance of advancing dynamic object detection in surveillance videos by introducing a novel framework that integrates Temporal Convolutional Networks (TCNs) and Federated Learning (FL) within edge computing environments. This research is motivated by the critical need for real-time threat response, enhanced security measures, and privacy preservation in dynamic surveillance scenarios. Leveraging TCNs, the system captures temporal dependencies, providing a comprehensive understanding of object movements. FL ensures decentralized model training, mitigating privacy concerns associated with centralized approaches. Current challenges in real-time processing, privacy preservation, and adaptability to dynamic environments are addressed through innovative solutions. Model optimization techniques optimize TCN efficiency, ensuring real-time processing. Advanced privacy-preserving mechanisms secure FL, addressing privacy concerns. Transfer learning and data augmentation enhance adaptability to dynamic scenarios. The proposed system not only addresses existing challenges but also contributes to the evolution of surveillance technology. Comprehensive metrics, including accuracy, sensitivity, specificity, and real-time processing metrics, provide a thorough evaluation of the system's performance. This research introduces an approach to dynamic object detection, combining TCN and FL in edge computing environments. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 98%, and F1 score reaching 96%.

With the fast development of Internet of Things (IoT) devices, it is urgently needed to understand the real-time cybersecurity risks posed to them actively. In the ever-growing field of IoT environments, Distributed Denial of Service (DDoS) threats pose an essential challenge, cooperating with the reliability of these methods. These attacks are usually utilized in real-time to write down e-commerce platforms, government websites, and banking systems. To deal with the DDoS attacks, there's an increased interest in decentralized learning methods, especially federated learning (FL), a newly acquired enhanced examination from the cyberattack cooperatively trained deep learning (DL) methods with dispersed cyber threats summaries. The recommendation of FL resolves the data privacy problem successfully. FL intends to form a global approach by allowing multi-participants with local information to train a similar method in a distributed way, with outcomes without replacing sample data. This paper presents a Metaheuristic-Driven Dimensionality Reduction for Robust Attack Defense Using Deep Learning Models (MDRRAD-DLM) in real-world IoT applications. The aim is to propose effective detection and mitigation strategies for DDoS attacks. The data preprocessing phase initially applies Z-score normalization to transform the input data into a standardized format. Furthermore, the parrot optimization (PO) technique is employed for the feature selection process to select the significant and relevant features from input data. Moreover, the temporal convolutional network and bi-directional gated recurrent unit with multi-head attention (TCN-MHA-Bi-GRU) technique is implemented for the attack classification process. Finally, the elk herd optimizer (EHO) technique fine-tunes the parameter selection of the TCN-MHA-Bi-GRU technique. The efficiency of the MDRRAD-DLM approach is examined under NSLKDD and CIC-IDS2017 datasets. The experimental validation of the MDRRAD-DLM approach portrayed a superior accuracy value of 99.14% and 99.41% over the dual datasets.

Federated learning (FL) and split neural networks (SplitNN) are state-of-art distributed machine learning techniques to enable machine learning without directly accessing raw data on clients or end devices. In theory, such distributed machine learning techniques have great potential in distributed applications, in which data are typically generated and collected at the client-side while the collected data should be processed by the application deployed at the server-side. However, there is still a significant gap in evaluating the performance of those techniques concerning their practicality in the Internet of Things (IoT)-enabled distributed systems constituted by resource-constrained devices. This work is the first attempt to provide empirical comparisons of FL and SplitNN in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, different model architectures, multiple clients, and various performance metrics. For the learning performance (i.e., model accuracy and convergence time), we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data. We show that the learning performance of SplitNN is better than FL under an imbalanced data distribution but worse than FL under an extreme non-IID data distribution. For implementation overhead, we mount both FL and SplitNN on Raspberry Pi devices and comprehensively evaluate their overhead, including training time, communication overhead, power consumption, and memory usage. Our key observations are that under the IoT scenario where the communication traffic is the primary concern, FL appears to perform better over SplitNN because FL has a significantly lower communication overhead compared with SplitNN. However, our experimental results also demonstrate that neither FL or SplitNN can be applied to a heavy model, e.g., with several million parameters, on resource-constrained IoT devices because its training cost would be too expensive for such devices. Source code is released and available: https://github.com/Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi.

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: 1) model parameters transmitted in each round may be used to recover private data, which leads to security risks; 2) not independent and identically distributed (non-IID) private data seriously adversely affect the training of FL (especially distillation-based FL); and 3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this article develops an intrusion detection method based on a semisupervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a model based on convolutional neural networks (CNNs) for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, the discriminator is designed to improve the quality of each client's predicted labels, and to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of the hard-label strategy and voting mechanism further reduces communication overhead. The experiments on the real-world traffic data set with three non-IID scenarios show that our proposed method can achieve better detection performance as well as lower communication overhead than state-of-the-art methods.

The healthcare industry, particularly with the advent of the Internet of Medical Things (IoMT), has witnessed significant integration of Internet of Things (IoT) technologies. IoMT is transforming healthcare by providing substantial benefits to both consumers and healthcare providers. However, the exponential growth in IoMT devices and their data generation raises critical challenges related to data analysis, security, and privacy. Traditional centralized artificial intelligence (AI) approaches, reliant on deep learning (DL) and machine learning (ML) algorithms, struggle to address the increasing complexity of sensitive medical data due to scalability and privacy concerns. Federated Learning (FL) emerges as a promising solution, enabling collaborative model training directly on IoMT devices while preserving data privacy by transmitting only model updates to central servers. This approach ensures data confidentiality and addresses privacy concerns associated with centralized systems. Despite its potential, research on FL in the context of IoMT remains limited. This paper examines the latest developments and innovations in FL, focusing on its application in IoMT and smart healthcare systems. It explores FL architectures, aggregation algorithms, frameworks, and their integration into IoMT‐driven healthcare applications. Additionally, the paper highlights challenges, including data heterogeneity, communication overhead, and security vulnerabilities, alongside privacy‐preserving techniques such as differential privacy, homomorphic encryption (HE), and secure multiparty computation (SMC). Finally, it identifies future research directions to advance FL‐powered IoMT solutions, offering valuable insights for academia and industry stakeholders aiming to enhance privacy‐preserving, intelligent healthcare systems.

The exponential growth of the Internet of Things (IoT) domain has raised the question of the immediate need for complex and privacy-sensitive data processing methodologies. Federated Learning (FL) makes learning decentralized and supports individual privacy; hence, it proves to be an optimistic approach. However, according to recent research, federated learning systems are vulnerable to attacks that may breach client privacy. Traditional federated learning models face issues such as verification of aggregated results and high computational and communication demands, making them less practical in large-scale IoT implementations. We propose a new federated learning framework: Verifiable Horizontal Federated Learning (VHFL). The main goals of VHFL include data privacy, reducing computational and communication-side overheads, and ensuring the accuracy of aggregated results. To improve the data’s privacy, VHFL uses single-mask encryption techniques together with group-key techniques. Another step will be to further integrate Latin Squares Design to reduce client-side computational and communication overheads. The system introduces a new verification scheme that generates Hamiltonian graphs from LSD to ensure that the VHFL aggregation result is correct. We tested the proposed system on several datasets, including MIMIC-III and HAR and compared to traditional federated learning models. More importantly, our proof-of-concept provided evidence that VHFL is robust in ensuring high efficiency and strong privacy within IoT environments. These experiments confirm that VHFL is efficient in handling the most demanding issues posed to federated learning and can be viable for both secure and efficient IoT applications.

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

Federated Learning (FL) is a recently invented distributed machine learning technique that allows available network clients to perform model training at the edge, rather than sharing it with a centralized server. Unlike conventional distributed machine learning approaches, the hallmark feature of FL is to allow performing local computation and model generation on the client side, ultimately protecting sensitive information. Most of the existing FL approaches assume that each FL client has sufficient computational resources and can accomplish a given task without facing any resource-related issues. However, if we consider FL for a heterogeneous Internet of Things (IoT) environment, a major portion of the FL clients may face low resource availability (e.g., lower computational power, limited bandwidth, and battery life). Consequently, the resource-constrained FL clients may give a very slow response, or may be unable to execute expected number of local iterations. Further, any FL client can inject inappropriate model during a training phase that can prolong convergence time and waste resources of all the network clients. In this paper, we propose a novel tri-layer FL scheme, Federated Proximal, Activity and Resource-Aware 31 Lightweight model (FedPARL), that reduces model size by performing sample-based pruning, avoids misbehaved clients by examining their trust score, and allows partial amount of work by considering their resource-availability. The pruning mechanism is particularly useful while dealing with resource-constrained FL-based IoT (FL-IoT) clients. In this scenario, the lightweight training model will consume less amount of resources to accomplish a target convergence. We evaluate each interested client's resource-availability before assigning a task, monitor their activities, and update their trust scores based on their previous performance. To tackle system and statistical heterogeneities, we adapt a re-parameterization and generalization of the current state-of-the-art Federated Averaging (FedAvg) algorithm. The modification of FedAvg algorithm allows clients to perform variable or partial amounts of work considering their resource-constraints. We demonstrate that simultaneously adapting the coupling of pruning, resource and activity awareness, and re-parameterization of FedAvg algorithm leads to more robust convergence of FL in IoT environment.

The emergence of the Internet of Things (IoT) has revolutionized service automation, enabling the development of smart applications. However, the vast interconnectivity of IoT devices not only produces large volumes of data but also creates multiple potential attack surfaces. While Machine Learning (ML) offers insights from IoT data, inherent data privacy and security challenges hinder its effective utilization. Federated Learning (FL) offers privacy-preserving ML for distributed edge devices. Nevertheless, the susceptibility to attacks poses a threat to the integrity of IoT data impacting ML for IoT services and applications. To tackle this challenge and identify IoT devices compromised by attacks like label-flipped data, this article introduces an innovative defense mechanism modeled after the human immune system. Analogous to ‘B’ cells, which detect viruses within the human body, the Reinforcement Learning (RL) agent identifies malicious IoT nodes that participate in federated learning enabled IoT. Subsequently, the FL server, similar to ‘T’ cells in Human immune systems eliminate/destroy infected cells, quarantines/discards the malicious IoT nodes (that are FL clients) and their reported parameters. Like ‘B’ cells and ‘T’ cells work together to defend the human body against infections and diseases, RL agent and FL server work together to defend/secure FL enabled IoT from compromised/malicious IoT devices. Specifically, with the help of Deep Reinforcement Learning (DRL), the RL agent continually monitors model updates from participating IoT nodes during training phase to find malicious nodes and then to isolate or remove those malicious nodes (i.e., parameters) at FL server while aggregating parameters for the global model. The effectiveness of the proposed approach is demonstrated through experiments, where RL agent detects malicious/compromised IoT nodes and FL server discards the parameters from such malicious/compromised IoT nodes. We evaluate our proposed approach using numerical results obtained from experiments where we observe that our approach outperforms the existing state-of-the-art approaches in terms of detection rate, error, and accuracy for enhancing IoT security in FL enabled IoT.

Communication-efficient semi-synchronous hierarchical federated learning with balanced training in heterogeneous IoT edge environments

Federated learning (FL) faces significant challenges in Internet of Things (IoT) networks due to device limitations in energy and communication resources, especially when considering the large size of FL models. From an energy perspective, the challenge is aggravated if devices rely on energy harvesting (EH), as energy availability can vary significantly over time, influencing the average number of participating users in each iteration. Additionally, the transmission of large model updates is more susceptible to interference from uncorrelated background traffic in shared wireless environments. As an alternative, federated distillation (FD) reduces communication overhead and energy consumption by transmitting local model outputs, which are typically much smaller than the entire model used in FL. However, this comes at the cost of reduced model accuracy. Therefore, in this paper, we propose FL-distillation alternation (FLDA). In FLDA, devices alternate between FD and FL phases, balancing model information with lower communication overhead and energy consumption per iteration. We consider a multichannel slotted-ALOHA EH-IoT network subject to background traffic/interference and compared FLDA to FL, FD, and SplitFed. In such a scenario, FLDA demonstrates higher model accuracy than both FL and FD, and achieves faster convergence than both FL and SplitFed. While SplitFed achieves a similar accuracy level to FLDA with no interference, the method is highly affected by interference, which also affects FL. Moreover, FLDA achieves target accuracies saving up to 98.02% in energy consumption relative to FL and up to 99.85% relative to SplitFed.