Research Article Approaches to Federated Computing for the Protection of Patient Privacy and Security Using Medical Applications Osman Sirajeldeen Ahmed, 1 Emad Eldin Omer, 2 Samar Zuhair Alshawwa , 3 Malik Bader Alazzam , 4 and Reefat Arefin Khan 5 1 Ajman University, College of Humanities and Sciences, UAE 2 Ajman University, College of Mass Communication, UAE 3 Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 4 Information Technology College, Ajloun National University, Jordan 5 IUBAT-International University of Business Agriculture and Technology, Dhaka, Bangladesh Correspondence should be addressed to Malik Bader Alazzam; m.alazzam@aau.edu.jo Received 23 December 2021; Revised 13 January 2022; Accepted 27 January 2022; Published 11 February 2022 Academic Editor: Fahd Abd Algalil Copyright © 2022 Osman Sirajeldeen Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computing model may train on a distributed dataset using Medical Applications, which is a distributed computing technique. Instead of a centralised server, the model trains on device data. The server then utilizes this model to train a joint model. The aim of this study is that Medical Applications claims no data is transferred, thereby protecting privacy. Botnet assaults are identified through deep autoencoding and decentralised traffic analytics. Rather than enabling data to be transmitted or relocated off the network edge, the problem of the study is in privacy and security in Medical Applications strategies. Computation will be moved to the edge layer to achieve previously centralised outcomes while boosting data security. Study Results in our suggested model detects anomalies with up to 98 percent accuracy utilizing MAC IP and source/destination/IP for training. Our method beats a traditional centrally controlled system in terms of attack detection accuracy. 1. Introduction While Badotra et al. [1] are credited with coining the term “Medical Applications”, the first description of its implementation can be found in [2]. Multiple devices work together to train a shared model in Medical Applications. Multiple clients’ parametric improvements are combined over numerous training rounds to achieve this. Several customers compete in each round to improve a globally available model using data that they have access to only locally. Because the models are supposed to be smaller than the dataset, Medical Applications lowers data transmission while simultaneously addressing privacy issues [3]. Also, all calculations may be done on the customers’ devices. Maintaining server farms, developing new models, and handling enormous datasets become simpler. While the round-based structure of Medical Applications implies models are smaller than data transmitted, it is feasible that large bandwidth may be required. In particular, mobile customers with restricted data access should have lower communication expenses. Many communication cost-saving strategies have emerged as a consequence. To address privacy issues, Medical Applications has several customers that compete in each round to construct a globally available model using data that they only have access to locally. While the round-based structure of Medical Applications implies models are smaller than data supplied, Hindawi Applied Bionics and Biomechanics Volume 2022, Article ID 1201339, 6 pages https://doi.org/10.1155/2022/1201339