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End-cloud collaborative computing framework ensures the security and privacy of edge device data, enabling collaborative training of global models without direct data exchange. However, in practical scenarios, anomalies in training or edge device data may severely degrade or disable the global model’s performance. Existing frameworks lack effective debugging and anomaly localization, hindering real-time monitoring and precise identification of abnormal edge devices in data heterogeneity scenarios. In this paper, we propose a new method named FedCheck, a debugging framework for end-cloud collaborative federated learning that enables real-time alerts and detects abnormal devices for nonindependent and identically distributed (nonIID) data without disrupting the regular training process. Specifically, we employ a model similarity-based method to quantitatively assess the degree of device anomaly in data heterogeneity scenarios, supporting real-time alerts during the end-cloud collaboration process. Furthermore, a simulation program replays the training process based on recorded telemetry data, facilitating backtracking debugging of any training round and the status of edge devices. Finally, the framework removes abnormal devices and repairs the global model. Experiments on MNIST and Fashion-MNIST datasets demonstrate that FedCheck can effectively detect and locate abnormal devices in data heterogeneity scenarios. Even in large-scale federated learning, it maintains high detection performance and exhibits good scalability.

Federated learning (FL) has been proposed to enable distributed learning on artificial intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Since random initial models in FL can easily result in unregulated stochastic gradient descent (SGD) processes, existing FL methods greatly suffer from both slow convergence and poor accuracy, especially in non-IID scenarios. To address this problem, we propose a novel method named CyclicFL, which can quickly derive effective initial models to guide the SGD processes, thus improving the overall FL training performance. We formally analyze the significance of data consistency between the pre-training and training stages of CyclicFL, showing the limited Lipschitzness of loss for the pre-trained models by CyclicFL. Moreover, we systematically prove that our method can achieve a faster convergence speed under various convexity assumptions. Unlike traditional centralized pre-training methods that require public proxy data, CyclicFL pre-trains initial models on selected AIoT devices cyclically without exposing their local data. Therefore, they can be easily integrated into any security-critical FL methods. Comprehensive experimental results show that CyclicFL can not only improve maximum classification accuracy by up to $14.11\%$, but also significantly accelerate the overall FL training process.

Protecting healthcare data privacy and security is crucial in advanced manufacturing, which involves medical devices. It encompasses patient records and clinical trial data. Federated learning emerges as a solution that enables model training across different institutions without compromising data privacy and security. However, existing frameworks often exhibit a bias towards clients with larger data volumes, neglecting the connection between global and local model performance. This can result in suboptimal aggregation of the global model, thereby affecting the effectiveness and efficiency of the overall process. To address these limitations, we propose a performance evaluation-driven federated learning framework (PedFed). The primary objective of PedFed is to enhance global model aggregation and improve communication efficiency. Our approach involves a client selection strategy based on performance evaluation of local and global models. Specifically, we introduce the concept of local model improvement (LMI) using Intersection over Union (IoU) for client selection in medical image segmentation scenarios. Moreover, we introduce a dynamic aggregation framework incorporating validation IoU as a weighting factor to mitigate model divergence caused by not independent and identically distributed (non-IID) data. We focus on performing image segmentation tasks to simulate the analysis of sensitive data in the healthcare domain. Experimental results conducted on brain tumor and heart segmentation datasets demonstrate the superiority of the PedFed framework over the baseline framework, confirming its benefits in communication efficiency.

Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural network, especially Generative Adversarial Network (GAN), is able to reproduce the original data from model parameters, i.e. gradient leakage problem. To solve this problem, SL provides a framework for secure aggregation using blockchain methods. In this paper, we consider the scenario of compromised and malicious participants in the SL environment, where a participant can manipulate the privacy of other participant in collaborative training. We propose a method, Swarm-FHE, Swarm Learning with Fully Homomorphic Encryption (FHE), to encrypt the model parameters before sharing with the participants which are registered and authenticated by blockchain technology. Each participant shares the encrypted parameters (i.e. ciphertexts) with other participants in SL training. We evaluate our method with training of the convolutional neural networks on the CIFAR-10 and MNIST datasets. On the basis of a considerable number of experiments and results with different hyperparameter settings, our method performs better as compared to other existing methods.

Traditional machine learning projects have revolved around training the model with the help of previously observed data to be able to predict output for future unknown data. In the current scenario, when the data generated are huge, centralized training of the model becomes inefficient. Hence, distributed approach with client server model has to be used for training the models. This introduces data handling and critical data privacy issues. This paper concentrates on Federated learning (FL) which builds a model for the server by aggregating the parameters obtained from the local models of the client devices. The research work focuses on design and evaluation of three new FL algorithms against the average of the performances of the local models. The evolved approach considering weights of the local models proportional to accuracy of the local model is found to be the most accurate and better than the centralized approach. The evaluation is done using three different algorithms belonging to regression and classification on multiple datasets. It is observed that there is only one round of communication between the clients and server required in the federated learning setup to achieve the benchmarked accuracy set by the centralized setup. This is a considerable development and state-of-the-art approach to reduce communication and computation costs.

Federated Learning (FL) enables multiple parties to train a global model collaboratively without sharing local data. However, a key challenge of FL is data distribution heterogeneity across participants, which causes model drift in local training and significantly reduces the model performance. To address this challenge, we analyze the inconsistency differences between different model layers of local models and further propose Layer-wise Distance Regularization (LWDR) and Layer-wise Momentum Aggregation (LWMA). The proposed LWDR and LWMA optimize the local training and model aggregation processes, respectively, to improve the convergence performance of FL on data in the nonindependent and identically distributed (Non-IID) scenarios. Our experiments on well-known datasets show that our algorithm significantly outperforms the state-of-the-art FL algorithms in convergence speed, accuracy, and stability in different Non-IID scenarios.

Nowadays, more and more federated learning algorithms have been implemented in edge computing, to provide various customized services for mobile users, which has strongly supported the rapid development of edge intelligence. However, most of them are designed relying on the reliable device-to-device communications, which is not a realistic assumption in the wireless environment. This paper considers a realistic aggregation problem for federated learning in a single-hop wireless network, in which the parameters of machine learning models are aggregated from the learning agents to a parameter server via a wireless channel with physical interference constraint. Assuming that all the learning agents and the parameter server are within a distance $\mathrm{\Gamma }$ from each other, we show that it is possible to construct a spanning tree to connect all the learning agents to the parameter server for federated learning within $O(log\mathrm{\Gamma })$ time steps. After the spanning tree is constructed, it only takes $O(log\mathrm{\Gamma })$ time steps to aggregate all the training parameters from the learning agents to the parameter server. Thus, the server can update its machine learning model once according to the aggregated results. Theoretical analyses and numerical simulations are conducted to show the performance of our algorithm.

As a widely used network security defense technology, network intrusion detection has more deep learning methods used to improve the performance of intrusion detection. However, this method requires a large-scale network traffic data set for training, increasing privacy leakage risk. In this paper, a network intrusion detection algorithm based on Gaussian differential privacy federated learning (NIDS-FLGDP) is proposed. NIDS-FLGDP adopts the client–server architecture of federated learning, introduces the differential privacy of the Gaussian mechanism to ensure the security of the calculation process, uses the improved FedAvg algorithm to reduce communication overhead, and uses the improved 1D CNN to participate in collaborative training for the local model. Optimal parameters for Gaussian differential privacy and the optimal number of participating clients were determined from experiments. Model accuracy rates for binary classification and multi-classification training NIDS-FLGDP are 0.97, 0.975, 0.97 and 0.97, 0.985, 0.96, respectively, for KDD CUP99, NSL_KDD, and UNSW_NB15 network intrusion detection datasets. The results show that NIDS-FLGDP improves intrusion detection performance while protecting network traffic privacy compared with the previous methods. Its applicability and effectiveness have been fully verified, which provides a practical reference for the safe processing and analysis of a large number of diversified network traffic data in the future.

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy.

Intelligent Traffic Management is a crucial issue closely related to daily life and productivity, with traffic congestion being a complex and challenging problem faced by most cities. Traffic Signal Control (TSC) stands out as the most direct and effective method to tackle congestion. It aims to minimize travel time, enhance throughput, improve traffic safety, reduce emissions, and conserve energy by coordinating the direction and timing of vehicle movements at intersections. Traditional TSC methods mostly rely on simple rules, limited data, and expert knowledge, making them inadequate for increasingly complex traffic scenarios. In the context of TSC, an increasing number of researchers are turning to Deep Learning (DL) methods to address identification, decision-making, and optimization challenges. Although many reviews have examined the TSC problems and the application of Reinforcement Learning in this field, there remains a notable gap in comprehensive analyses of TSC utilizing a wider range of DL techniques, including Deep Reinforcement Learning, Federated Learning, and Meta-learning. This paper, building upon the basic concepts and traditional approaches of TSC, provides a detailed overview of the latest research advancements employing different DL methods for this issue. Experimental settings and evaluations are also introduced. Furthermore, to spark new interest in this research field, future works are proposed.

Intrusion detection based on federated learning allows the sharing of more high-quality attack samples to improve the intrusion detection performance of local models while preserving the privacy of local data. Most research on federated learning intrusion detection requires local models to be homogeneous. However, in practical scenarios, local models often include both homogeneous and heterogeneous models due to differences in hardware capabilities and business requirements among nodes. Additionally, there is still room for improvement in the accuracy of recognizing novel attacks in existing researches. To address the challenges mentioned above, we propose a Group-based Federated Knowledge Distillation Intrusion Detection approach. First, through a step-by-step grouping method, we achieve the grouping effect of intra-group homogeneity and inter-group heterogeneity, laying the foundation for reducing the aggregation difficulty in intra-group homogenous aggregation and inter-group heterogeneous aggregation. Second, in intra-group homogenous aggregation, a dual-objective optimization model is employed to quantify the learning quality of local models. Weight coefficients are assigned based on the learning quality to perform weighted aggregation. Lastly, in inter-group heterogeneous aggregation, the group leader model’s learning quality is used to classify and aggregate local soft labels, generating global soft labels. Group leader models utilize global soft labels for knowledge distillation to acquire knowledge from heterogeneous models. Experimental results on NSL-KDD and UNSW-NB datasets demonstrate the superiority of our proposed method over other algorithms.

EEG is a personal privacy information, that is difficult to share data and not conducive to the study of sleep staging. In this paper, the Federated Learning (FL) method is introduced to solve the problem of data island in sleep staging EEG signals. To simulate the distribution of different user data, we introduce the K-means algorithm into EEG dataset partitioning and transform it into a non-independent and identically distributed unbalanced dataset. Then, based on the classic Multilayer Perceptron (MLP) network, we analyse the privacy leakage problem in FL, and Differential Privacy (DP) is used to protect the privacy of user EEG data. Next, we analyse the reason for the accuracy loss between FL and Centralized Learning (CL), and the effect of the imbalance of the data distribution of each client. We propose a self-adaptive batch method to adaptively balance the batch size of each client, and experiments show that the training results of imbalanced datasets can be improved. Finally, when the accuracy of the model does not reach the historical best value in the set epoch interval, the Mandatory Optimization Strategy (MOS) is used to further improve the accuracy and reduce the randomness of the model. The DP-FL based on the self-adaptive batch and MOS strategy is tested on our dataset and the Sleep-EDF Database Expanded dataset, which not only protects user privacy but also achieves the same accuracy as CL. The accuracy is 82.88% on our dataset and 91.81% on the subset of the Sleep-EDF Database Expanded dataset.

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

Medical cancer rehabilitation healthcare center data maintenance is a global challenge with increased mortality risk. The Internet of Things (IoT)-based applications in healthcare were implemented through sensors and various connecting devices. The main problem of this procedure is the privacy of data, which is the biggest challenge with IoT, as all the connected devices transfer data in real time, the integration of multiple and other protocols can be hacked by the end-to-end connection, and it is not secure, security issues may crop up due to handling of such massive data in real time. Recent studies showed that a more structured risk assessment is needed to secure the medical cancer rehabilitation healthcare center data maintenance. In this respect, collaborative learning frameworks, such as Deep Federated Collaborative Learning (DFCL), are implemented for the study of medical cancer rehabilitation healthcare center data maintenance based on IoT-based systems and are proposed with smart short-term Bayesian convolution network systems for data analysis. This DFCL approach has been preferred in this context, strengthening privacy by allowing sensitive data to be retained. Experiments on benchmark datasets demonstrate that the federated model balances fairness, privacy, and accuracy. In this paper, we analyze administrative data count by medical stages taken from 2016 to 2022, the administrative data include data for routine operations. It is frequently used to assess by achieving an accuracy range of 19.8%. The leading diagnoses taken as per the patient’s cost and stay count identifying a disease, illness, or problem by examining the unusual combination of symptoms made an accurate diagnosis which is 26% more efficient than the leading diagnosis. The hospital dictionary analysis is based on dictionary analysis count and data visualization summary; accuracy is 50% higher than the existing data visualization summary. By comparing the hospital dictionary, home health care analysis shows a 44.5% efficient analysis rate for patient data maintenance. Moreover, the adult day-care centers analyzed 88.6% efficient analysis rate for patient data maintenance with 750 patients.

Federated learning (FL) is an important approach to cooperate with multiple devices for learning without exchanging data between devices and central server. However, due to bandwidth and other reasons, the communication efficiency should be considered when the volume of information transmitted is limited. In this paper, we utilize the tool of lattice quantization form quantization theory and the variable intercommunication interval to improve communication efficiency. Meanwhile, to make strong privacy guarantee, we incorporate the notion of differential privacy (DP) to the FL framework with local SGD algorithm. By adding calibrated noises, we propose a universal lattice quantization for differentially private federated averaging algorithm (ULQ-DP-FedAvg). We provide tight privacy bound by using some privacy techniques. We also analyze the convergence bound of ULQ-DP-FedAvg based on bits rate constraints and the growing inter-communication interval as well as the data are non-independent identically distribution (Non-IID). It turns out that the algorithm converges and preserves that the privacy has scarcely influenced on the convergence rate. The effectiveness of our algorithm is demonstrated by synthetic and real datasets.

Under a federated learning environment, the training samples are generally collected and stored locally on each client’s device, which makes the machine learning procedure not meet the requirement of independent and identical distribution (IID). Existing federated learning methods to deal with non-IID data generally assume that the data is globally balanced. However, real-world multi-class data tend to exhibit long-tail distribution, where the majority of samples are in a few head classes and a large number of tail classes only have a small amount of data. This paper, therefore, focuses on addressing the problem of handling non-IID and globally long-tailed data in a federated learning scenario. Accordingly, we propose a new federated learning method called Federated meta re-weighting networks (FedReN), which assigns weights during the local training process from the class-level and instance-level perspectives, respectively. To deal with data non-IIDness and global long-tail, both of the two re-weighting functions are globally trained by the meta-learning approach to acquire the knowledge of global long-tail distribution. Experiments on several long-tailed image classification benchmarks show that FedReN outperforms the state-of-the-art federated learning methods. The code is available at https://github.com/pxqian/FedReN.

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

Recent advancements in neuroimaging techniques have sparked a growing interest in understanding the complex interactions between anatomical regions of interest (ROIs), forming into brain networks that play a crucial role in various clinical tasks, such as neural pattern discovery and disorder diagnosis. In recent years, graph neural networks (GNNs) have emerged as powerful tools for analyzing network data. However, due to the complexity of data acquisition and regulatory restrictions, brain network studies remain limited in scale and are often confined to local institutions. These limitations greatly challenge GNN models to capture useful neural circuitry patterns and deliver robust downstream performance. As a distributed machine learning paradigm, federated learning (FL) provides a promising solution in addressing resource limitation and privacy concerns, by enabling collaborative learning across local institutions (i.e., clients) without data sharing. While the data heterogeneity issues have been extensively studied in recent FL literature, cross-institutional brain network analysis presents unique data heterogeneity challenges, that is, the inconsistent ROI parcellation systems and varying predictive neural circuitry patterns across local neuroimaging studies. To this end, we propose FedBrain, a GNN-based personalized FL framework that takes into account the unique properties of brain network data. Specifically, we present a federated atlas mapping mechanism to overcome the feature and structure heterogeneity of brain networks arising from different ROI atlas systems, and a clustering approach guided by clinical prior knowledge to address varying predictive neural circuitry patterns regarding different patient groups, neuroimaging modalities and clinical outcomes. Compared to existing FL strategies, our approach demonstrates superior and more consistent performance, showcasing its strong potential and generalizability in cross-institutional connectome-based brain imaging analysis. The implementation is available here.

Federated learning (FL) is an approach to machine learning (ML) in which the training data is not managed centrally. In the era of data-driven decision-making, the financial industry faces a unique conundrum: how to leverage the power of ML without compromising the privacy and security of sensitive financial data. FL, an innovative ML paradigm, emerges as a transformative solution to this challenge. This chapter provides an overview of the profound implications and promising applications of FL within the financial sector. Data is retained by data parties that participate in the FL process and is not shared with any other entity. This makes FL an increasingly popular solution for ML tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory, or practical reasons.

Federated learning (FL) is a type of collaborative learning used to train machine-learning (ML) models. It is important from a privacy and data security perspective. It provides faster access to data spread across multiple organizations, devices, and locations. Smart phones are one of the FL-based solutions that helps in personalizing the user experience by maintaining data privacy. FL uses locally generated data to train ML models on devices and shares models without sending raw data to a remote server. Several protocols, such as Federated Averaging, Federated Stochastic Gradient Descent (SGD), Federated Meta-Learning, and Federated Distillation, exist to supplement FL, which plays a significant role in training ML models. Selection, configuration and reporting are the three important phases of FL protocols. In this chapter, a brief review on various protocols and approaches of FL is discussed.