Fuzzy Consensus With Federated Learning Method in Medical Systems

Large-scale group decision-making (LSGDM) is one of the main open problems where a decision is made by many different results. Moreover, there is also a problem with how to make the decision when there is no all information. This uncertainty can be very problematic for many different solutions in artificial intelligence. In this paper, we propose to extend a federated learning (FL) approach to not only a training process but also for making a decision using many different classifiers. This solution is applied in LSGDM, where many different results are intended for the classification of various data and can be used for deciding, even when some of the data are missing. For this purpose, we propose a fuzzy consensus that can be used in these problems. The contribution of this paper is the new way of using FL and extending its operation to many different classifiers. Our proposition was described for medical purposes and evaluated to show the advantages of the proposal. The proposal obtained 89,12% of accuracy on HAM10000, which is one of the best results compared to state-of-art.

[1]  Gautam Srivastava,et al.  Agent architecture of an intelligent medical system based on federated learning and blockchain technology , 2021, J. Inf. Secur. Appl..

[2]  Zeshui Xu,et al.  Managing noncooperative behaviors in large-scale group decision-making with linguistic preference orderings: The application in Internet Venture Capital , 2021, Inf. Fusion.

[3]  Muhammad Attique Khan,et al.  Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification , 2021, Comput. Electr. Eng..

[4]  Muhammad Sharif,et al.  Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework , 2021, Pattern Recognit. Lett..

[5]  Yong Deng,et al.  Network-based evidential three-way theoretic model for large-scale group decision analysis , 2021, Inf. Sci..

[6]  Humberto Bustince,et al.  A cohesion-driven consensus reaching process for large scale group decision making under a hesitant fuzzy linguistic term sets environment , 2021, Comput. Ind. Eng..

[7]  Wei Zhang,et al.  Federated learning for machinery fault diagnosis with dynamic validation and self-supervision , 2021, Knowl. Based Syst..

[8]  Changming Sun,et al.  Cascade knowledge diffusion network for skin lesion diagnosis and segmentation , 2021, Appl. Soft Comput..

[9]  Zhihan Lv,et al.  Intelligent edge computing based on machine learning for smart city , 2021, Future Gener. Comput. Syst..

[10]  Kai Fan,et al.  Anonymous and Privacy-Preserving Federated Learning With Industrial Big Data , 2021, IEEE Transactions on Industrial Informatics.

[11]  Supeng Leng,et al.  Federated Learning Empowered End-Edge-Cloud Cooperation for 5G HetNet Security , 2021, IEEE Network.

[12]  Natalia Wawrzyniak,et al.  Identification of Vessels on Inland Waters Using Low-Quality Video Streams , 2021, HICSS.

[13]  Sunghwan Park,et al.  FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs , 2021, Sensors.

[14]  Frank Gauterin,et al.  Probabilistic Predictions with Federated Learning , 2020, Entropy.

[15]  Sebastián Ventura,et al.  Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study , 2020, Medical Image Anal..

[16]  A Inés,et al.  Biomedical image classification made easier thanks to transfer and semi-supervised learning , 2020, Comput. Methods Programs Biomed..

[17]  Aabid Rashid Wani,et al.  Big data based hybrid machine learning model for improving performance of medical Internet of Things data in healthcare systems , 2021 .

[18]  Healthcare Paradigms in the Internet of Things Ecosystem , 2021 .

[19]  Daekook Kang,et al.  Normal wiggly hesitant fuzzy set with multi-criteria decision making problem , 2020 .

[20]  Shin'ya Obara,et al.  Battery control for leveling the amount of electricity purchase in smart‐energy houses , 2020, International Journal of Energy Research.

[21]  Priyanka Chawla,et al.  Medical Internet of things using machine learning algorithms for lung cancer detection , 2020 .

[22]  Yejun Xu,et al.  Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization , 2020, Information Sciences.

[23]  Sunil Kumar Singla,et al.  Study of Fusion of medical images and classification comparison using different kernels of SVM and K-NN classifiers , 2020, 2020 First IEEE International Conference on Measurement, Instrumentation, Control and Automation (ICMICA).

[24]  R. Udendhran,et al.  Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers , 2020, Soft Comput..

[25]  Hayat Khaloufi,et al.  Fog Computing in the Age of Big Healthcare Data: Powering the Medical Internet of Things , 2020 .

[26]  Christine Moore,et al.  Medical Internet of Things-based Healthcare Systems: Wearable Sensor-based Devices, Patient-Generated Big Data, and Real-Time Clinical Monitoring , 2020 .

[27]  Ali Hassan Sodhro,et al.  A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients , 2020, IEEE Access.

[28]  Sebastiao Simões da Cunha,et al.  A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making , 2019, Inverse Problems in Science and Engineering.

[29]  Yifan Sun,et al.  Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: A review of three decades of research with recent developments , 2019, Expert Syst. Appl..

[30]  Dawid Połap,et al.  Analysis of Skin Marks Through the Use of Intelligent Things , 2019, IEEE Access.

[31]  Wiphada Wettayaprasit,et al.  Convolutional Neural Networks Using MobileNet for Skin Lesion Classification , 2019, 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[32]  Sara Nasiri,et al.  Deep-CLASS at ISIC Machine Learning Challenge 2018 , 2018, ArXiv.

[33]  Harald Kittler,et al.  Descriptor : The HAM 10000 dataset , a large collection of multi-source dermatoscopic images of common pigmented skin lesions , 2018 .

[34]  Papanikolopoulos,et al.  Computer Aided Diagnosis of Skin Lesions from Morphological Features , 2018 .

[35]  R. Kucharski,et al.  The representativity index of a simple monitoring network with regular theoretical shapes and its practical application for the existing groundwater monitoring network of the Tychy-Urbanowice landfills, Poland , 2016, Environmental Earth Sciences.

[36]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[38]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.