Federated Learning-Based Privacy Preservation with Blockchain Assistance in IoT 5G Heterogeneous Networks


  • Sampathkumar Arumugam Department of Computer Science, Dambi Dollo University, Ethiopia, and Department of Applied Cybernetics, Faculty of Science, University of Hradec Kralove, Czech https://orcid.org/0000-0001-5318-5676
  • Shishir Kumar Shandilya Visiting Researcher, Liverpool Hope University, UK, and VIT Bhopal University, India
  • Nebojsa Bacanin Singidunum University, Danijelova 32, Belgrade, 11000, Serbia




Blockchain, 5G network, federated machine, privacy preservation, registration.


In the area where privacy is of greater concern, federated learning,a distributed machine learning strategy for preserving privacy,is widely employed in several privacy concern applications. In the meantime, neural architectures became familiar with deep learning approaches for automatic tuning of the architecture of deep neural networks (DNN). While searching with neural architecture and federated learning has experienced several challenges, optimized neural architecture research in federated learning is extensively on demand. DNN faces numerous issues while training such user privacy and ensuring the integrity of the aggregated results obtained from a server. To provide solutions for the above-mentioned issues, enormous federated learning techniques worked towards preserving privacy and were applied in different situations. Still, it is an open challenge that enables users to verify if the cloud server functions appropriately while ensuring users’ privacy while training. Federated Learning Method is a new way to improve the accuracy and precision, since the previous approach failed to opt the solutions. Here, Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL)is proposed to ensure the confidentiality of users’ local gradients while performing federated learning. The parameters such as classification accuracy, running time, Communication overhead, Computation overhead, and transaction speed are considered. The values obtained for these parameters are compared against three standard methods, namely Biparing Method (BM) Homomorphic Cryptosystem (HC), and Multiple Authorities with Attribute-Based Signature scheme (MA-ABS)against proposed Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL). As a result, the proposed ECC-BFL achieved 95% of classification accuracy, 65 sec of running time, 76% of communication overhead, 63% of computation overhead, and 92% of transaction speed.


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Author Biographies

Sampathkumar Arumugam, Department of Computer Science, Dambi Dollo University, Ethiopia, and Department of Applied Cybernetics, Faculty of Science, University of Hradec Kralove, Czech

Sampathkumar Arumugam has received his Bachelor’s in Information Technology in 2009; Master’s in Mainframe Technology in 2012 and completed Ph.D. degree in Anna University Chennai in 2019. He has 10 years of academic experience in various repudiated institution. He has published more than 10 SCI articles and more than 13 Scopus articles in peer-reviewed journals. He has been editor in the some of the book series and published several book chapters in springer and Elsevier which are Scopus indexed. He has published Indian and Australian government patents. He has been actively participating as reviewers in some of the international journals and member of CSI societies. His research interest includes Bioinformatics, Artificial Intelligence, Data Mining, Machine Learning, Data Analytic and Optimization Techniques.

Shishir Kumar Shandilya, Visiting Researcher, Liverpool Hope University, UK, and VIT Bhopal University, India

Shishir Kumar Shandilya is the Division Head of Cyber Security and Digital Forensics at VIT Bhopal University. He is working as a Principal Consultant to the Govt. of India for Technology Development and Assessment in Cyber Security. He is also a Visiting Researcher at Liverpool Hope University-United Kingdom, a Cambridge University Certified Professional Teacher and Trainer, ACM Distinguished Speaker and a Senior Member of IEEE. He is a NASSCOM Certified Master Trainer for Security Analyst SOC (SSC/Q0909: NVEQF Level 7) and an Academic Advisor to National Cyber Safety and Security Standards, New Delhi. He has received the IDA Teaching Excellence Award for distinctive use of technology in Teaching by Indian Didactics Association, Bangalore (2016) and Young Scientist Award for two consecutive years, 2005 and 2006, by Indian Science Congress and MP Council of Science and Technology. He has seven books published by Springer Nature-Singapore, IGI-USA, River-Denmark and Prentice Hall of India. His recently published book is on Advances in Cyber Security Analytics and Decision Systems by Springer. Dr. Laxman Singh obtained his B. Tech in Electronics and Communication Engineering from C.R. State (Govt.) College of Engineering, Murthal, Sonipat (Haryana) and M.Tech in Instrumentation and Control from M.D. University, Haryana, India in 2004 and 2009 respectively. He received his PhD degree from Jamia Millia Islamia (a central Govt. of India University) in 2016. Presently he is working as Associate Professor in the Department of Electronics & Communication Engineering at Noida Institute of Engineering & Technology (NIET), Greater Noida. He has total teaching experience of more than seventeen years. Dr. Laxman Singh has published about 35 research articles in the field of image processing, AI, and machine learning in various refereed international/national journals as well as in international conferences of repute. His current research interests are in the areas of Wavelet analysis, Artificial Intelligence, Image processing, and Optimization techniques.

Nebojsa Bacanin, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia

Nebojsa Bacanin received his Ph.D. degree from Faculty of Mathematics, University of Belgrade 1 in 2015 (study program Computer Science, average grade 10,00). He started University career in Serbia 13 years ago at Graduate School of Computer Science in Belgrade. He currently works as an associate professor and as a vice-dean at Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia.

He is involved in scientific research in the field of computer science and his specialty includes stochastic optimization algorithms, swarm intelligence, soft-computing and optimization and modeling, as well as artificial intelligence algorithms, swarm intelligence, machine learning, image processing and cloud and distributed computing. He has published more than 120 scientific papers in high quality journals and international conferences indexed in Clarivate Analytics JCR, Scopus, WoS, IEEExplore, and other scientific databases, as well as in Springer Lecture Notes in Computer Science and Procedia Computer Science book chapters. He has also published 2 books in domains of Cloud Computing and Advanced Java Spring Programming.

He is a member of numerous editorial boards, scientific and advisory committees of international conferences and journals. He is a regular reviewer for international journals with high Clarivate Analytics and WoS impact factor such as Journal of Ambient Intelligence & Humanized Computing, Soft Computing, Applied Soft Computing, Information Sciences, Journal of Cloud Computing, IEEE Transactions on Computers, IEEE Review, Swarm and Evolutionary Computation, Journal of King Saud University “C Computer and Information Sciences, SoftwareX, Neurocomputing, Operations Research Perspectives, etc. He actively participates in 1 national and 1 international projects from the domain of computer science. He has also been included in the prestigious Stanford University list with 2% best world researchers for the year 2020.


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