In recent years, novel technologies in smart healthcare systems have opened significant opportunities for diagnosis and treatment across various medical fields. Federated Learning (FL), a decentralized machine learning approach, trains shared models using local data from devices like wearables and hospital systems without transferring sensitive information, offering a promising solution to privacy challenges in areas such as cancer prediction, COVID-19 detection, drug discovery, and medical image processing. This literature survey reviews FL architectures (e.g., FedHealth, PerFit), applications, and recent advancements, demonstrating their impact on healthcare through enhanced predictive models for patient care. Key findings include improved accuracy in wearable-based diagnostics and secure multi-institutional collaboration, though limitations persist. We also highlight open challenges, such as security risks, communication costs, and data heterogeneity, which require further research attention.

In recent years, Internet of Healthcare Things (IoHT) devices have attracted significant attention from computer scientists, healthcare professionals, and patients. These devices enable patients, especially in areas without access to hospitals, to easily record and transmit their health data to medical staff via the Internet. However, the analysis of sensitive health information necessitates a secure environment to safeguard patient privacy. Given the sensitivity of healthcare data, ensuring security and privacy is crucial in this sector. Federated learning (FL) provides a solution by enabling collaborative model training without sharing sensitive health data with third parties. Despite FL addressing some privacy concerns, the privacy of IoHT data remains an area needing further development. In this paper, we propose a privacy-preserving federated learning framework to enhance the privacy of IoHT data. Our approach integrates federated learning with $ϵ$-differential privacy to design an effective and secure intrusion detection system (IDS) for identifying cyberattacks on the network traffic of IoHT devices. In our FL-based framework, SECIoHT-FL, we employ deep neural network (DNN) including convolutional neural network (CNN) models. We assess the performance of the SECIoHT-FL framework using metrics such as accuracy, precision, recall, F1-score, and privacy budget ($ϵ$). The results confirm the efficacy and efficiency of the framework. For instance, the proposed CNN model within SECIoHT-FL achieved an accuracy of 95.48% and a privacy budget ($ϵ$) of 0.34 when detecting attacks on one of the datasets used in the experiments. To facilitate the understanding of the models and the reproduction of the experiments, we provide the explainability of the results by using SHAP and share the source code of the framework publicly as free and open-source software.

As the landscape of big data evolves, the paradigm of data sharing and exchanging has gained paramount importance. Nonetheless, the transition to efficient data sharing and exchanging is laden with challenges. One of the principal challenges is incentivizing diverse users to partake in the data sharing and exchange process. Users, especially those in potential competitive positions, often exhibit reluctance towards sharing or exchanging their data, particularly if they perceive the rewards as inadequate. Given this context, it’s imperative to institute an incentive mechanism that’s not only computationally efficient and secure but also provides both monetary and trustworthy inducements. This study introduces a taxonomy of incentive-based data sharing and exchanging, structured around its lifecycle, and elucidates the challenges inherent in each phase. We classify incentive mechanisms into monetary and non-monetary categories, postulating that the concomitant use of both types of incentives is more effective for data sharing and exchanging applications. Subsequent sections provide an overview of extant literature pertinent to each phase of the data sharing and exchanging lifecycle. In conclusion, we underscore the prevailing challenges in this domain and advocate for intensified efforts to refine the design of incentive mechanisms in data sharing and exchanging.

Machine learning has brought about a revolutionary transformation in healthcare. It has traditionally been employed to create predictive models through training on locally available data. However, privacy concerns can sometimes impede the collection and integration of data from diverse sources. Conversely, a lack of sufficient data may hinder the construction of accurate models, thereby limiting the ability to produce meaningful outcomes. Especially in the field of healthcare, collecting datasets centrally is challenging due to privacy concerns. Indeed, federated learning (FL) emerges as a sophisticated distributed machine learning approach that comes to the rescue in such scenarios. It allows multiple devices hosted at different institutions, like hospitals, to collaboratively train a global model without sharing raw data. In addition, each device retains its data securely on locally, addressing the challenges of time-consuming annotation and privacy concerns. In this paper, we conducted a comprehensive literature review aimed at identifying the most advanced federated learning applications in cancer research and clinical oncology analysis. Our main goal was to present a comprehensive overview of the development of federated learning in the field of oncology. Additionally, we discuss the challenges and future research directions.