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1. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging

   https://doi.org/10.1148/ryai.240637  

   Koutsoubis, Nikolas; Waqas, Asim; Yilmaz, Yasin; Ramachandran, Ravi P; Schabath, Matthew B; Rasool, Ghulam (May 2025, Radiology: Artificial Intelligence)

   Artificial Intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patient data remain a major barrier to the widespread adoption of AI in clinical practice, as large and diverse training datasets are essential for developing accurate, robust, and generalizable AI models. Federated Learning offers a privacy-preserving solution by enabling collaborative model training across institutions without sharing sensitive data. Instead, model parameters, such as model weights, are exchanged between participating sites. Despite its potential, federated learning is still in its early stages of development and faces several challenges. Notably, sensitive information can still be inferred from the shared model parameters. Additionally, postdeployment data distribution shifts can degrade model performance, making uncertainty quantification essential. In federated learning, this task is particularly challenging due to data heterogeneity across participating sites. This review provides a comprehensive overview of federated learning, privacy-preserving federated learning, and uncertainty quantification in federated learning. Key limitations in current methodologies are identified, and future research directions are proposed to enhance data privacy and trustworthiness in medical imaging applications 

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2. Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care

   https://doi.org/10.1371/journal.pdig.0000117  

   Rajendran, Suraj; Xu, Zhenxing; Pan, Weishen; Ghosh, Arnab; Wang, Fei (March 2023, PLOS Digital Health)

   Frasch, Martin G. (Ed.)

   With the wider availability of healthcare data such as Electronic Health Records (EHR), more and more data-driven based approaches have been proposed to improve the quality-of-care delivery. Predictive modeling, which aims at building computational models for predicting clinical risk, is a popular research topic in healthcare analytics. However, concerns about privacy of healthcare data may hinder the development of effective predictive models that are generalizable because this often requires rich diverse data from multiple clinical institutions. Recently, federated learning (FL) has demonstrated promise in addressing this concern. However, data heterogeneity from different local participating sites may affect prediction performance of federated models. Due to acute kidney injury (AKI) and sepsis’ high prevalence among patients admitted to intensive care units (ICU), the early prediction of these conditions based on AI is an important topic in critical care medicine. In this study, we take AKI and sepsis onset risk prediction in ICU as two examples to explore the impact of data heterogeneity in the FL framework as well as compare performances across frameworks. We built predictive models based on local, pooled, and FL frameworks using EHR data across multiple hospitals. The local framework only used data from each site itself. The pooled framework combined data from all sites. In the FL framework, each local site did not have access to other sites’ data. A model was updated locally, and its parameters were shared to a central aggregator, which was used to update the federated model’s parameters and then subsequently, shared with each site. We found models built within a FL framework outperformed local counterparts. Then, we analyzed variable importance discrepancies across sites and frameworks. Finally, we explored potential sources of the heterogeneity within the EHR data. The different distributions of demographic profiles, medication use, and site information contributed to data heterogeneity. 

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3. Federated Fuzzy Clustering for Longitudinal Health Data

   Balkus, Salvador; Fang, Hua; Wang, Honggang (December 2022, IEEEACM Conference on Connected Health Applications Systems and Engineering Technologies CHASE)

   Traditional implementations of federated learning for preserving data privacy are unsuitable for longitudinal health data. To remedy this, we develop a federated enhanced fuzzy c-means clustering (FeFCM) algorithm that can identify groups of patients based on complex behavioral intervention responses. FeFCM calculates a global cluster model by incorporating data from multiple healthcare institutions without requiring patient observations to be shared. We evaluate FeFCM on simulated clusters as well as empirical data from four different dietary health studies in Massachusetts. Results find that FeFCM converges rapidly and achieves desirable clustering performance. As a result, FeFCM can promote pattern recognition in longitudinal health studies across hundreds of collaborating healthcare institutions while ensuring patient privacy. 

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4. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization

   Amirhossein Reisizadeh, Aryan Mokhtari (January 2020, International Conference on Artificial Intelligence and Statistics)

   Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized. This framework faces several systems-oriented challenges which include (i) communication bottleneck since a large number of devices upload their local updates to a parameter server, and (ii) scalability as the federated network consists of millions of devices. Due to these systems challenges as well as issues related to statistical heterogeneity of data and privacy concerns, designing a provably efficient federated learning method is of significant importance yet it remains challenging. In this paper, we present FedPAQ, a communication-efficient Federated Learning method with Periodic Averaging and Quantization. FedPAQ relies on three key features:(1) periodic averaging where models are updated locally at devices and only periodically averaged at the server;(2) partial device participation where only a fraction of devices participate in each round of the training; and (3) quantized message-passing where the edge nodes quantize their updates before uploading to the parameter server. These features address the communications and scalability challenges in federated learning. We also show that FedPAQ achieves near-optimal theoretical guarantees for strongly convex and non-convex loss functions and empirically demonstrate the communication-computation tradeoff provided by our method. 

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5. Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction

   https://doi.org/10.3390/biomedicines12081916  

   Hasan, Md Rakibul; Li, Qingrui; Saha, Utsha; Li, Juan (August 2024, Biomedicines)

   Diabetes is a global epidemic with severe consequences for individuals and healthcare systems. While early and personalized prediction can significantly improve outcomes, traditional centralized prediction models suffer from privacy risks and limited data diversity. This paper introduces a novel framework that integrates blockchain and federated learning to address these challenges. Blockchain provides a secure, decentralized foundation for data management, access control, and auditability. Federated learning enables model training on distributed datasets without compromising patient privacy. This collaborative approach facilitates the development of more robust and personalized diabetes prediction models, leveraging the combined data resources of multiple healthcare institutions. We have performed extensive evaluation experiments and security analyses. The results demonstrate good performance while significantly enhancing privacy and security compared to centralized approaches. Our framework offers a promising solution for the ethical and effective use of healthcare data in diabetes prediction. 

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