Anichur Rahman, Md. Sazzad Hossain, Ghulam Muhammad, Dipanjali Kundu et al.
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 vulne...
Abdullah Al Omar, Mohammad Shahriar Rahman, Anirban Basu, Shinsaku Kiyomoto
Shahidul Islam Khan, Abu Sayed Md. Latiful Hoque
Abstract In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeh...
Xianglin Bao, Cheng Su, Yan Xiong, Wenchao Huang et al.
Federated learning (shorted as FL) recently proposed by Google is a privacy-preserving method to integrate distributed data trainers. FL is extremely useful due to its ensuring privacy, lower latency, less power consumption and smarter models, but it could fail if multiple trainers abort training or...
Md Mamunur Rashid, Shahriar Usman Khan, Fariha Eusufzai, Md. Azharuddin Redwan et al.
The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing Io...
K. M. Jawadur Rahman, Faisal Ahmed, Nazma Akhter, Mohammad Al Hasan et al.
Federated Learning (FL) is a new technology that has been a hot research topic. It enables training an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. There are many application domains where large amounts of properly labeled and c...
Adam A. Alli, Muhammad Mahbub Alam
Computation offloading is one of the important application in Internet of Things (IoT) ecosystem. Computational offloading provides assisted means of processing large amounts of data generated by abundant IoT devices, speed up processing of intensive tasks and save battery life. In this paper, we pr...
Chandramohan Dhasaratha, Mohammad Kamrul Hasan, Shayla Islam, Shailesh Khapre et al.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each...
Md. Rayhan Ahmed, A.K.M. Muzahidul Islam, Swakkhar Shatabda, Salekul Islam
Identity Management System (IDMS) refers to how users or individuals are identified and authorized to use organizational systems and services. Since traditional identity management and authentication systems rely heavily on a trusted central authority, they cannot mitigate the effects of single poin...
Moinul Islam, Md Tanzim Reza, Mohammed Kaosar, Mohammad Zavid Parvez
Medical institutions often revoke data access due to the privacy concern of patients. Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased global model based on collecting updates from local models trained by client’s data while keeping the local data private. T...
Anichur Rahman, Kamrul Hasan, Dipanjali Kundu, Md. Jahidul Islam et al.
Taki Hasan Rafi, Faiza Anan Noor, Tahmid Hussain, Dong‐Kyu Chae
Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder, V. B. Surya Prasath
Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federa...
Md Fahimuzzman Sohan, Anas Basalamah
Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from the beginning of its invention. The eye-catching feature of FL is handling data in a decentralized manner which creates a privacy preserving environment in Artificial Intelligence (AI) applications. ...
Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan et al.
Federated learning (FL) is a promising way to use the computing power of mobile devices while maintaining the privacy of users. Current work in FL, however, makes the unrealistic assumption that the users have ground-truth labels on their devices, while also assuming that the server has neither data...