Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data

Yan, Zengqiang; Wicaksana, Jeffry; Wang, Zhiwei; Yang, Xin; Cheng, Kwang-Ting

doi:10.1109/jbhi.2020.3040015

Cited by 85 publications

(58 citation statements)

References 19 publications

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“…HFL data partition is quite common in FL applied for medical applications. More than half of FL studies on medical applications implemented horizontal medical data partition in their experiment [18,19,21,37,[39][40][41][42][43][44][45][46][47][48][49]51,52,54,55]. Unlike FL applied for nonmedical applications where training is carried out across many nodes, FL studies in medical applications only handle limited nodes from 2 to 100, as listed in Table 2.…”

Section: Horizontal Federated Learning (Hfl)mentioning

confidence: 99%

“…The perturbation method preserves private data and model privacy by adding a controlled random noise to the training data or the machine learning model parameters during the training process. For instance, differential privacy [18,43,44,55] and hybrid exchange parameters [39] algorithms are the perturbations techniques implemented in the FL studies published in medical applications. In comparison, the encryption method preserves private data and model privacy by encrypting the parameters exchanged and the gradients in the aggregation process in the FL environment, such as the homomorphic encryption algorithm [20,21,51].…”

Section: Data Privacy Protections For Federated Learningmentioning

confidence: 99%

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A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

et al. 2021

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Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.

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Section: Horizontal Federated Learning (Hfl)mentioning

confidence: 99%

Section: Data Privacy Protections For Federated Learningmentioning

confidence: 99%

A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

et al. 2021

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“…Federated learning is applied in many fields with its privacy protection. In [7], Yan et al applied federated learning to variant perceptual learning of multisource decentralized medical image data and constructed small medical image data sets of different institutions into large medical image data sets. Kang et al integrated federated learning modules into mobile networks to enable mobile devices to train and share models without leaking their local data [8].…”

Section: Federated Learningmentioning

confidence: 99%

BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Peng

Chen

et al. 2021

Mobile Information Systems

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Applications of Internet of Vehicles (IoV) make the life of human beings more intelligent and convenient. However, in the present, there are some problems in IoV, such as data silos and poor privacy preservation. To address the challenges in IoV, we propose a blockchain-based federated learning pool (BFLP) framework. BFLP allows the models to be trained without sharing raw data, and it can choose the most suitable federated learning method according to actual application scenarios. Considering the poor computing power of vehicle systems, we construct a lightweight encryption algorithm called CPC to protect privacy. To verify the proposed framework, we conducted experiments in obstacle-avoiding and traffic forecast scenarios. The results show that the proposed framework can effectively protect the user's privacy, and it is more stable and efficient compared with traditional machine learning technique. Also, we compare the CPC algorithm with other encryption algorithms. And the results show that its calculation cost is much lower compared to other symmetric encryption algorithms.

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“…Ref. [ 45 ] also built its solution using the decentralized approach in working on medical images that resided in different sources. The proposition acknowledges that the variation of medical images, combined with the limited number of medical images, would cause significant variation of parameters updated by clients, leading to bad convergence after aggregation.…”

Section: Fl Architectures In Ehealthmentioning

confidence: 99%

“…EHR-based (e.g., medical images) prediction models [37] EHR application to predict hospitalizations for patients with heart diseases [26] EHR application for medical whole-brain segmentation [38] EHR-based model [39] federated learning for prediction of insurances [40] electronic healthcare records mining [41] application of learning in pharmaceuticals discovery [42,43] use federated learning in medical data segmentation [44] utilizes the features of federated learning in smart manufacturing and healthcare [45] decentralized FL approach working on medical images [26] BrainTorrent: a FL framework to train a complex fully CNN in a decentralized fashion using whole-brain image segmentation Telesurgery and its design requirements [46] Tactile Robotic for Telesurgery [47] Telesurgery and its enabling technologies [48] Communication requirements for telesegury [49] Tactile-based Telesurgery [30] Communication requirements for telesegury [50] Holography for telesurgery [51] Holography applications toward medical field…”

Section: Ref Focusmentioning

confidence: 99%

Rebirth of Distributed AI—A Review of eHealth Research

Khan

Alkaabi

2021

Sensors

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The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.

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Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data

Cited by 85 publications

References 19 publications

A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Rebirth of Distributed AI—A Review of eHealth Research

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