Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Long, Guodong; Shen, Tao; Tan, Yue; Gerrard, Leah; Clarke, Allison; Jiang, Jing

doi:10.1007/978-3-030-72188-6_6

Cited by 36 publications

(12 citation statements)

References 27 publications

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“…Heterogeneous model architecture is another major challenging scenario of FL. The recently proposed KD-based FL (Lin et al 2020;Jeong et al 2018;Li and Wang 2020;Long et al 2021) can serve as an alternative solution to address this challenge. In particular, with the assumption of adding a shared toy dataset in the federated setting, these KD-based FL methods can distill knowledge from a teacher model to student models with different model architectures.…”

Section: Heterogeneous Federated Learningmentioning

confidence: 99%

FedProto: Federated Prototype Learning across Heterogeneous Clients

Tan

Long

Liu

et al. 2022

AAAI

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Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For example, clients may differ in terms of data distribution, network latency, input/output space, and/or model architecture, which can easily lead to the misalignment of their local gradients. To improve the tolerance to heterogeneity, we propose a novel federated prototype learning (FedProto) framework in which the clients and server communicate the abstract class prototypes instead of the gradients. FedProto aggregates the local prototypes collected from different clients, and then sends the global prototypes back to all clients to regularize the training of local models. The training on each client aims to minimize the classification error on the local data while keeping the resulting local prototypes sufficiently close to the corresponding global ones. Moreover, we provide a theoretical analysis to the convergence rate of FedProto under non-convex objectives. In experiments, we propose a benchmark setting tailored for heterogeneous FL, with FedProto outperforming several recent FL approaches on multiple datasets.

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Section: Heterogeneous Federated Learningmentioning

confidence: 99%

FedProto: Federated Prototype Learning across Heterogeneous Clients

Tan

Long

Liu

et al. 2022

AAAI

Self Cite

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“…As a result, this strategy can protect the privacy of patient information by using an iterative process in which model parameters are constantly exchanged and modified, but raw data is not disclosed. As a result, to preserve the security and privacy of the hospital system, FL provides a privacypreserving method while also providing the benefits of machine learning models to solve numerous difficulties related to healthcare systems [193,210].…”

Section: Role Of Flmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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“…However, the survey did not present any practical tools and frameworks to simulate the experimental parameters. Authors in [20] proposed a FLbased digital privacy health framework, that can collect data from collaborative hospital consortium setups. During the data collection, anonymization, and DP are induced into the data.…”

Section: Existing Surveysmentioning

confidence: 99%

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel¹,

Bhattacharya

Tanwar

et al. 2022

IEEE Access

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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 FLbased 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.

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Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Cited by 36 publications

References 27 publications

FedProto: Federated Prototype Learning across Heterogeneous Clients

FedProto: Federated Prototype Learning across Heterogeneous Clients

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

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