Optimization of fluid volume control in hemodialysis using federated learning

In recent years, automated discovery of thoracic diseases from Chest X-ray images (CXR) has become an essential field of computer-aided analysis. The diversity and scarcity of medical data make it complex to generate a precise global classification in the healthcare sector. The main reasons are privacy issues and legal obstacles limiting data sharing between healthcare institutions. On the contrary, data from a single source is hardly enough for developing a universal analysis model. Therefore, Federated Learning (FL) is a potential solution for data diversity and privacy issues. This research uses the FL approach to effectively learn from multiclass and heterogeneous medical data. However, the FL approach is susceptible to adversarial attacks; therefore, blockchain (BC) is integrated to enhance the security and privacy of patient data. This paper proposes the aggregator model, i.e., two-layer Long Short-Term Memory (2LLSTM) with the federated proximal term (FedProx), namely 2LLSTMFP, for enhancing the classification. The proximal term integrated with the 2LLSTM solves the issues created by data heterogeneity and increases stability during classification. 2LLSTMFP aggregates the classified information from the Convolutional Neural Network (CNN). The incorporated FP term develops the utilisation of proximal operators to update 2LLSTMFP weights regularly. The updated 2LLSTMFP weights regularise the effect of adversarial updates with BC, which further improves its robustness to malicious devices, i.e., adversarial attacks. The developed FLBC-2LLSTMFP is evaluated by using the ChestX-ray14 dataset. The FLBC 2LLSTMFP is analysed regarding accuracy, specificity, precision, recall, F1-score, False Positive Rate (FPR), and False Negative Rate (FNR). The existing research MobileLungNetV2 and MLRFNet are used to compare with the FLBC-2LLSTMFP. The accuracy of FLBC-2LLSTMFP is 99.98%, which is higher than that of MobileLungNetV2 and MLRFNet.

Security and privacy of patient data are critical concerns in the healthcare system. This research aims to investigate the impact of implementing blockchain technology on enhancing the security and privacy of patient data in healthcare. Using the SmartPLS analysis method, this study empirically examines the relationships between blockchain technology implementation and data security and data privacy within the healthcare system. The research sample consists of healthcare organizations that have implemented blockchain technology for data management. Data is collected through surveys and analyzed using SmartPLS to assess the effects of blockchain technology on data security and privacy. The findings reveal the positive influence of blockchain technology implementation on enhancing the security and privacy of patient data. The study also identifies challenges, such as scalability and interoperability, that need to be addressed for successful implementation. This research contributes to the existing literature by providing empirical evidence on the benefits and challenges of implementing blockchain technology to safeguard patient data in healthcare systems.

Abstract: In the field of machine learning, federated learning (FL) has become a breakthrough paradigm, provided a decentralized method of training models while solving issues with data security, privacy, and scalability. This study offers a thorough analysis of FL, including an examination of its underlying theories, its varieties, and a comparison with more conventional machine learning techniques. We explore the drawbacks of conventional machine learning techniques, especially when sensitive and distributed data is involved. We also explain how FL addresses these drawbacks by leveraging collaborative learning across decentralized devices or servers. We also highlight the various fields in which FL finds application, including healthcare, industries, IoT, mobile devices, and education, demonstrating its potential to deliver tailored services and predictive analytics while maintaining data privacy. Furthermore, we address the main obstacles to FL adoption, such as costly communication, heterogeneous systems, statistical heterogeneity, and privacy concerns, and we suggest possible directions for future research to effectively overcome these obstacles. In order to facilitate future study and growth in this quickly developing discipline, this review attempts to shed light on the advances and challenges of FL.

Privacy and data security have become the new hot topic for regulators in recent years. As a result, Federated Learning (FL) (also called collaborative learning) has emerged as a new training paradigm that allows multiple, geographically distributed nodes to learn a Deep Learning (DL) model together without sharing their data. Blockchain is becoming a new trend as data protection and privacy are concerns in many sectors. Technology is leading the world and transforming into a global village where everything is accessible and transparent. We have presented a blockchain enabled security model using FL that can generate an enhanced DL model without sharing data and improve privacy through higher security and access rights to data. However, existing FL approaches also have unique security vulnerabilities that malicious actors can exploit and compromise the trained model. The FL method is compared to the other known approaches. Users are more likely to choose the latter option, i.e., providing local but private data to the server and using ML apps, performing ML operations on the devices without benefiting from other users’ data, and preventing direct access to raw data and local training of ML models. FL protects data privacy and reduces data transfer overhead by storing raw data on devices and combining locally computed model updates. We have investigated the feasibility of data and model poisoning attacks under a blockchain-enabled FL system built alongside the Ethereum network and the traditional FL system (without blockchain). This work fills a knowledge gap by proposing a transparent incentive mechanism that can encourage good behavior among participating decentralized nodes and avoid common problems and provides knowledge for the FL security literature by investigating current FL systems.

Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security, quality and effective learning. This paper focuses on algorithms such as, federated averaging algorithm, differential privacy, federated stochastic variance and reduced gradient (FSVRG). To achieve data privacy and security, this research paper presents the main data statistics with the help of graphs, visual images and design models. Later, data security in federated learning models is researched and case studies are presented to identify risks and possible solutions. Detecting security gaps is a challenge for many companies. This paper presents solutions for the identification of security-related issues which results in a decrease in time complexity and an increase in accuracy. This research sheds light on the topics of federated learning and data security.

Purpose The purpose of this study is to develop a scalable and privacy-preserving predictive maintenance framework for wind farms by addressing the challenges of heterogeneous multi-component degradation. By combining stochastic physics-based models with machine learning techniques under a federated learning (FL) paradigm, the framework aims to provide accurate estimation of remaining useful life (RUL) for critical components while preserving data confidentiality. Furthermore, a multi-objective optimization layer leverages prognostic outputs to minimize maintenance costs and maximize turbine availability, offering an intelligent solution for enhancing reliability and efficiency in modern wind energy infrastructures. Design/methodology/approach This study proposes an integrated framework for predictive maintenance in wind farms, addressing multi-component degradation through the combination of FL and hybrid degradation models. Under variable wind and load conditions, critical wind turbine components such as gearboxes, bearings and generators exhibit heterogeneous and nonlinear degradation behaviors that challenge conventional maintenance approaches. The proposed framework integrates stochastic physics-based degradation modeling with data-driven learning techniques to enable accurate and interpretable estimation of the RUL at the component level. FL is employed to facilitate decentralized model training while preserving data privacy, allowing turbine-level models to collaboratively improve prognostic performance without the need for centralized data aggregation. Simulation-based evaluation demonstrates that the proposed adaptive framework achieves a reduction in cumulative wear of approximately 35% compared with static maintenance strategies, while increasing the Weibull characteristic life by nearly 49%. In addition, the framework reduces total maintenance cost by about 22%, improves estimation accuracy by lowering the Kalman filter mean absolute error by approximately 44% and increases system availability by more than 140% relative to static policies. These results confirm that the proposed approach provides a scalable, privacy-preserving and cost-efficient solution for predictive maintenance and reliability enhancement in distributed wind farm environments. Findings The study demonstrates that integrating hybrid degradation models with FL significantly improves the accuracy of RUL estimation for wind turbine components under heterogeneous operating conditions. Simulation results reveal that the proposed framework reduces wear progression, lowers maintenance costs and enhances overall system reliability compared to traditional centralized or periodic strategies. The federated approach ensures data privacy and reduces communication overhead, while the optimization layer effectively balances cost efficiency and turbine availability. These findings highlight the framework’s potential as a scalable, intelligent and privacy-preserving solution for predictive maintenance in modern wind farms. Research limitations/implications This study is based on simulation experiments and may not fully capture the complexities of real-world wind farm operations, such as unmodeled environmental influences or rare failure modes. The FL framework requires consistent data quality and communication infrastructure across turbines, which may limit applicability in regions with poor connectivity. Future work should involve large-scale field validation, incorporation of more diverse degradation mechanisms and integration with real-time supervisory control systems. Despite these limitations, the framework offers a strong foundation for advancing intelligent, distributed and privacy-preserving predictive maintenance in modern wind energy infrastructures. Practical implications The proposed framework provides wind farm operators with a scalable tool to optimize maintenance scheduling while reducing downtime and operational costs. By accurately estimating the RUL of critical components such as gearboxes, bearings and generators, operators can shift from fixed-interval maintenance to condition-based strategies, extending component lifespan and improving asset reliability. The FL approach preserves data privacy, enabling collaboration across multiple sites without centralizing sensitive operational data. This enhances trust and data security while supporting widespread adoption, making the framework highly applicable for industrial deployment in modern wind energy infrastructures. Social implications The adoption of intelligent predictive maintenance in wind farms contributes to greater energy security and sustainability by ensuring higher system reliability and reduced downtime. Improved efficiency in wind energy production supports the global transition toward clean energy, lowering dependence on fossil fuels and reducing greenhouse gas emissions. By preserving data privacy through FL, the framework also fosters trust and collaboration between operators, regulators and stakeholders. Ultimately, this approach strengthens public confidence in renewable energy technologies, promotes green job creation and accelerates the achievement of climate and sustainability goals at both regional and global levels. Originality/value This study introduces a novel predictive maintenance framework that integrates hybrid degradation models with FL to address the challenges of heterogeneous, multi-component wear in wind farms. Unlike traditional centralized or periodic strategies, the framework enables accurate and interpretable RUL predictions while preserving data privacy and reducing communication costs. The addition of a multi-objective optimization layer ensures cost-efficient and availability-driven maintenance scheduling. This combination of decentralized learning, physics-informed modeling and optimization provides a scalable, intelligent and privacy-preserving solution, offering significant value for advancing maintenance practices in modern renewable energy infrastructures.

BackgroundDeep learning (DL) is promising to detect glaucoma. However, patients’ privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model...

Federated learning (FL) offers collaborative machine learning across decentralized devices while safeguarding data privacy. However, data security and privacy remain key concerns. This paper introduces "Secure Federated Learning with a Homomorphic Encryption Model," addressing these challenges by integrating homomorphic encryption into FL. The model starts by initializing a global machine learning model and generating a homomorphic encryption key pair, with the public key shared among FL participants. Using this public key, participants then collect, preprocess, and encrypt their local data. During FL Training Rounds, participants decrypt the global model, compute local updates on encrypted data, encrypt these updates, and securely send them to the aggregator. The aggregator homomorphic ally combines updates without revealing participant data, forwarding the encrypted aggregated update to the global model owner. The Global Model Update ensures the owner decrypts the aggregated update using the private key, updates the global model, encrypts it with the public key, and shares the encrypted global model with FL participants. With optional model evaluation, training can iterate for several rounds or until convergence. This model offers a robust solution to Florida data privacy and security issues, with versatile applications across domains. This paper presents core model components, advantages, and potential domain-specific implementations while making significant strides in addressing FL's data privacy concerns.

Preserving user data privacy is paramount for airlines. However, achieving highly accurate predictions of passenger satisfaction while safeguarding the privacy of individual companys data remains a considerable challenge. To achieve this objective, we utilized a privacy-preserving machine learning technique known as Federated Learning (FL). FL allows model training on decentralized devices while ensuring data security and privacy. The FL process comprises client training processes, communication rounds, and weight aggregation. We simulate FL principles using multiple processes to enable distributed learning. Clients preprocess data and train local models, ensuring data privacy. Communication rounds involve clients downloading the global model, local training, and transmitting updates to a central server. Weight aggregation methods like Federated Averaging merge these updates, preserving data privacy. Additionally, we leverage Artificial Neural Networks (ANNs) as foundational techniques. ANNs consist of input, hidden, and output layers, with weight adjustments based on real value differences to achieve accuracy. Our approach combines FL with ANNs to demonstrate FLs potential in privacy-preserving predictive analytics. We use the Airline Passenger Satisfaction dataset for modeling and evaluate the impact of neural network depth and submodel quantity on prediction accuracy. Our experimental results reveal that neither the depth of neural networks nor the number of submodels significantly affects prediction accuracy. FL emerges as a promising approach to balance data privacy and prediction accuracy effectively.

Artificial intelligence has significantly enhanced disease diagnosis in healthcare, particularly through Deep Learning (DL) and Federated Learning (FL) approaches. These technologies have shown promise in detecting ocular diseases using medical imaging while addressing challenges related to data privacy and security. FL enables collaborative learning without sharing sensitive medical data, making it an attractive solution for healthcare applications. This systematic review aims to analyze the advancements in AI-driven ocular disease detection, with a particular focus on FL-based approaches. The article evaluates the evolution, methodologies, challenges, and effectiveness of FL in enhancing diagnostic accuracy while ensuring data confidentiality. The systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure transparency and reliability. Research articles published between 2017 and 2024 were identified using academic databases, including Web of Science, Scopus, IEEE Xplore, and PubMed. Studies focusing on DL and FL models for detecting ocular diseases were selected based on predefined inclusion and exclusion criteria. A comparative analysis of the methodologies, architectures, datasets, and performance metrics of different FL models has been presented. The findings indicated that FL preserves data privacy while achieving diagnostic performance comparable to traditional centralized AI models. Various FL models, including FedAvg and FedProx, have been implemented for ocular disease detection, with high accuracy and efficiency. However, challenges, such as data heterogeneity, communication efficiency, and model convergence, persist. FL represents a promising approach for ocular disease detection, balancing diagnostic accuracy with data privacy. Future research may focus on optimizing FL frameworks for improving scalability, communication efficiency, and integrating advanced privacy-preserving techniques.

A survey on federated learning for security and privacy in healthcare applications

Deep reinforcement learning based resource provisioning for federated edge learning

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.

The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient’s data which contributes effectively to the training process. The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FL-based electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.

This review explored data security and privacy in Internet of Health Things (IoHT) networks, focusing on local training for AI models and Federated Learning (FL) to ensure privacy and integrity. This highlights the importance of leveraging technological advancements for disease prediction and data exchange. Functional programming can be used in various industries and applications under real-world scenarios. In the medical industry, safeguarding the confidentiality of patient records and medical status is essential. This is where collaborative or federated learning becomes relevant. On the other hand, creating an intelligent system that helps medical personnel without exposing data can result in a Federated Learning concept. An example is an AI-based intelligent system for diagnosing brain tumors, which can effectively operate within a teamwork setting. Advancements in smart devices and applications designed for the Internet of Healthcare Things have created an ideal setting for implementing Machine Learning techniques. Nevertheless, conventional ML solutions work by collecting and processing data in a centralized manner. Federated Learning (FL) offers a potential solution for training machine-learning models on numerous separate devices without the need to share private data. As a result, FL provides a secure framework for managing extremely sensitive data in the context of the IoHT. This survey provides a detailed overview of new data security and privacy tools for Federated Learning in Internet of Health Things networks. Initially, we introduced the fundamental concepts of Federated Learning (FL) as it is applied to the IoHT.