Modeling Asynchronous Federated Learning for Health care application

The privacy and security of medical data is the most important aspect of Health Information System (HIS) as healthcare is totally reliant on data for treatment and research. The main purpose of HIS is to improve treatment processes by providing the latest information about the patient. The healthcare data consist of medical history, path lab records, medication history, diagnosis and treatment details, genetic and sexual information and profession. The privacy of the patient is becoming a concern for healthcare organizations because of the increasing threats and attacks. Moreover, using blockchain-based infrastructure would empower the patients whilst maintaining the security and privacy of the data and providing quality healthcare services. Healthcare providers are moving towards decision-making processes for the advancement in diagnostics, which require the availability and accessibility of accurate data, which is provided by HIS. The healthcare systems will be technically advanced to provide real-time patient monitoring.

In the swiftly evolving realm of healthcare technology, safeguarding the privacy and security of delicate medical data is of utmost importance. Conventional methods often prove inadequate in preserving patients’ confidentiality, compelling the need for innovative solutions. This research introduces a pioneering methodology called Selective Data Access through Privacy-Enhanced Blockchain. This approach integrates blockchain technology with advanced data analytics techniques, revolutionizing medical data management. By harnessing blockchain, the system restricts access to essential data, enhances privacy, and expedites predictive modeling. The attained outcomes are substantial: a potential 85% enhancement in data privacy, ensuring limited access to sensitive medical information, coupled with an estimated 80% reduction in processing time and resources for streamlined predictive modeling. Moreover, there is a prospective 70% cost saving in data storage and management. These results collectively underscore the transformative potential of the Selective Data Access through Privacy-Enhanced Blockchain methodology, promising optimized security, efficiency, and cost-effectiveness in healthcare data management.

Lung cancer continues to be one of the most widespread and deadly cancer diagnoses that affects humans worldwide. Early detection of lung cancer leads to decreased mortality rates; however, several challenges hinder the development and deployment of effective predictive models. These challenges consist of mainly the problem of high computational power to evaluate large-scale medical data, privacy and security of medical data, limited data sharing between medical organizations and interpretability to handle the black box problem that AI-based models face. Such limitations have posed severe difficulties to the utilization of conventional approaches in the prediction of lung cancer, thus limiting them most importantly for general use, especially in clinical practice settings in real time. To address these challenges, this research introduced a novel lung cancer prediction model that utilizes an integrated framework combining MapReduce, Private Blockchain, Federated Learning (FL), and Explainable Artificial Intelligence (XAI). It improves lung cancer detection using MapReduce to handle large lung cancer datasets, supporting rapid and scalable learning. Private Blockchain is used for the secure, tamper-proof, and immutable processing of patient information, whereas FL allows healthcare sectors to train models together, without compromising patients’ privacy. Moreover, it also employed XAI to improve the model’s interpretability so clinicians can understand and rely on AI predictions. Together, these methods improve AI’s efficiency and trustworthiness in medical applications. This proposed model provides better and more secure lung cancer predictions, ensuring interpretability and collaboration. With an exceptional accuracy of 98.21% and a miss rate of just 1.79%, it outperforms previously published approaches, establishing a new benchmark for privacy-preserving, explainable, and scalable AI models in healthcare.

The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. However, traditional learning models are not suitable for live scenarios that require privacy, data diversity, and decentralized processing. The Federated learning-based model facilitates the protection of medical data privacy while processing a large volume of medical images, aiming to improve the overall efficiency of the model. This paper proposes a Federated Learning based Ensemble Model (FLEM) framework for an efficient diagnosis of lung diseases. The FLEM utilizes explainable AI techniques, including SHAP, Grad-CAM, and Differential Privacy, to provide transparency and interpretability of predictions while maintaining the privacy and security of medical data. We applied InceptionV3, Conv2D, VGG16, and ResNet-50 models on the COVID-19, TB, and pneumonia datasets and analysed the performance of the models in FLEM and Central Server-based Learning Model (CSLM). The performance analysis shows that the FLEM model outperformed the traditional CSLM model in terms of accuracy, training time, and bandwidth consumption. CSLM witnesses a quicker convergence time than FLEM. Although the CSLM model converged after a considerable number of epochs, it resulted in a 5, 8, 9, and 10% accuracy reduction compared to the FLEM-based training of InceptionV3, Conv2D, VGG16, and ResNet50 that achieved accuracies of 91.8, 88, 92.5, and 95.5%, respectively.

With the development of medical data informatization, the data security, data privacy, and data controllability of electronic medical data with patient's personal privacy have gained social attention. Blockchain technology has decentralized characteristics, and the development of blockchain technology provides an effective solution for the secure sharing of e-medical data. Many scholars have proposed e-medical data security sharing solutions based on blockchain technology, and these solutions have attempted to solve the security problems encountered in sharing traditional e-medical data. By summarizing and analyzing the schemes in this field, this study analyzes the advantages, disadvantages, and challenges that the current mainstream schemes have, mainly in terms of blockchain data security storage, access control security sharing, and so on, in order to promote the further development of the e-medical data security sharing scheme based on blockchain technology. This study analyzes the blockchain-based e-medical data security sharing scheme proposed by scholars by analyzing the blockchain technology, cryptography technology, smart contract technology, P2P network technology and other related topics. Through systematic analysis and comparison, it explores the challenges of the current research and provides references for the future proposals of blockchain technology-based schemes.

Investigating the literature reveals the fact that the key management protocols play a vital role in protecting the security and privacy of medical data in telecare medical information systems. Recently, Tseng et al . have proposed an interesting elliptic curve cryptosystem (ECC) based self-certified key management scheme that can yield secure channel for the communications of secure sensors (cluster members) and access point (cluster head). After careful consideration, we found that their scheme suffers from the cluster head impersonation, replay, and key replicating attacks. In addition, in the secure session phase, there are some errata and a point multiplication that is undefined in the ECC and hence is mathematically incorrect. Finally, yet importantly, their presented scheme has been adopted for a similar application by other authors with the same problem. Therefore, in this paper, we first try to elaborate the existing errata and security threats. Second, we propose a modified version, which is free from the challenges of their scheme. Eventually, we propose a novel anonymous ECC-based self-certified two-factor key management scheme that not only provides the desired security features, but also has much better efficiency than several recently-published schemes, such as the presented one by Tseng et al . Our formal security verification and proof besides the efficiency analysis support our claim.

Venous thromboembolism (VTE) is a common vascular disorder requiring extended anticoagulation therapy postdischarge to reduce recurrence risk. Home rehabilitation management systems that use electronic health records from hospital care provide opportunities for continuous patient monitoring. However, transferring medical data from clinical to home settings raises significant concerns about privacy and security. Conventional methods such as manual data entry, optical character recognition, and dedicated data transmission lines face notable technical and operational challenges. This study aims to develop a QR code-based security transmission algorithm using Avro and byte pair encoding (BPE). The algorithm supports the secure creation and transfer of out-of-hospital health records by enabling patients to scan QR codes via a dedicated mobile app, ensuring data security and user privacy. Between January and October 2024, 300 hospitalized patients with VTE were recruited at the Sixth Medical Center of the Chinese PLA General Hospital. Post discharge, participants used a home rehabilitation app tailored for VTE management. The QR code-based security transmission algorithm was developed to securely transfer in-hospital electronic health records to the out-of-hospital app. It uses BPE, Avro, and Gzip for optimized data compression and uses ChaCha20 and BLAKE3 for encryption and authentication. Specifically, BPE tokenizes medical text, while Avro serializes JSON (JavaScript Object Notation) objects, contributing to data encryption. A proprietary tokenizer was trained, and compression efficiency was evaluated using a "Performance Benchmark Dataset." Comparative analyses were conducted to assess the compression efficiency of JSON serialization methods (Avro and ASN.1 [Abstract Syntax Notation One]), and tokenization algorithms (BPE and unigram). The dataset consisted of JSON files from 300 patients, averaging 240.1 fields per file (range 89-623) and 7095 bytes in size (range 2748-17,425 bytes). Using the BPE + Avro + Gzip algorithm, the average file size was reduced to 1048 bytes, achieving a compression ratio of 6.67. This was 1.82 times more efficient than traditional Gzip compression (average file size: 1907 bytes; compression ratio: 3.66; P<.001). For Chinese medical text tokenization, BPE outperformed unigram with a compression ratio of 4.68 versus 4.55 (P<.001). Avro and ASN.1 demonstrated comparable compression ratios of 2.57 and 2.59, respectively, when used alone (P=.30). However, Avro combined with BPE and Gzip significantly outperformed ASN.1, achieving compression ratios of 6.67 versus 5.21 (P<.001). Additionally, 84.7% (254/300) of patients needed to scan only 1 QR code, requiring an average of 3.1 seconds. The QR code-based security transmission algorithm using Avro and BPE efficiently compresses and transmits data in an encrypted manner and authenticates the identity of the scanning users, ensuring the privacy and security of medical data. Delivered as a software development kit, the algorithm offers straightforward implementation and usability, supporting its broad adoption across various applications.

As a novel distributed machine learning approach, federated learning (FL) is proposed to train a global model while preserving data privacy. However, some studies manifest that adversaries can still recover private information from the gradients. Differential privacy (DP) is a rigorous mathematical tool to protect records in a database against leakage. It has been widely applied in FL by perturbing the gradients. Nevertheless, while using DP in FL, the convergence performance of the global model is inevitably degraded. In this paper, we implement a DP-based FL scheme, which achieves local DP (LDP) by adding well-designed Gaussian noise on the gradients before clients upload them to the server. After that, we propose two strategies to improve the convergence performance of the DP-based FL. Both methods are realized by modifying the local objective function to limit the effect of LDP noise on convergence without degrading the privacy protection level. We then provide the detailed framework which adopts the LDP scheme and two strategies. The framework on different machine learning models is tested by simulation results, which show that our framework can improve the convergence performance up to 40% faster under different noise compared with other DP-based FL. Finally, we show the theoretical convergence guarantee of our proposed framework by first presenting the expected decrease in the global loss function for one round of training and then providing the upper convergence bound after multiple communication rounds.

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.

The rapid development of the Internet of Things technology has led to a boom in the adoption of intelligent healthcare management systems in the healthcare industry. However, it has also highlighted key issues such as security, privacy, and efficient query of medical data. Traditional methods for querying medical data suffer from severe data leakage risks, low query performance, and excessive storage space. This article proposes a comprehensive Secure ENcrypted Search for Health Scheme (SENSH) solution based on consortium blockchain and searchable encryption to address these challenges. SENSH enables efficient authorization management through Bloom filters, ensuring fast querying of large datasets by authorized users while saving storage space. It uses off-chain Advanced Encryption Standard (AES) and on-chain storage management for data protection, significantly reducing the likelihood of data exposure. The system is also enhanced with event triggering and logging mechanisms to support real-time monitoring and data tracing to meet audit compliance requirements. It provides version control and timestamping to accommodate dynamic data updates, employs an obfuscationfactor to prevent tag-based original data content leakage, and supports dynamic updating of tags to accommodate different access requirements. Experimental results show that SENSH excels in authorization management, privacy protection, defense against tampering, and anti-replay and Distributed Denial of Service (DDoS). Compared with existing schemes, SENSH has significant advantages in terms of gas consumption, computation cost, and execution time. It is particularly suited for the protection and efficient query of medical and health data.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.

Federated learning (FL) has been vigorously promoted in wireless edge networks as it fosters collaborative training of machine learning (ML) models while preserving individual user privacy and data security. In conventional FL, user equipment (UE) and an aggregator can collaboratively train shared global ML models by transmitting interactive ML models. In wireless edge networks, heterogeneity of multi-dimensional resources (e.g., computing and communication resources) used to train and transmit FL models may introduce stragglers, characterized by a slow update and/or transmission of local models. The stragglers can significantly degrade learning performance of FL, as the slowest participating UE can dramatically slow down the entire convergence. In this paper, to alleviate the negative impact of stragglers, we propose a dynamic straggler-aware clustering based FL (FeDSC) mechanism via adaptive UE clustering. Specifically, we first group participating UEs into multiple clusters based on their available computing and wireless resources. Then, we propose an adaptive clustering scheme to synchronously update the cluster aggregation model. Meanwhile, an edge server performs global aggregation of different cluster models in an asynchronous manner. Finally, we theoretically demonstrate the convergence of our proposed mechanism via numerical results. Numerical results show that our proposed mechanism can effectively reduce training time and wireless bandwidth consumption, while improving training efficiency and guaranteeing learning accuracy.

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches’ future directions and existing drawbacks are discussed in detail.