Thapar Institute of Engineering and Technology | India
Mr. Rajesh Kumar Chaudhary is a dedicated and result-oriented academician, serving as an Assistant Professor in the Department of Computer Science and Engineering. With over five years of academic and research experience, he has established expertise in clustered federated learning, wireless sensor networks, nature-inspired optimization algorithms, and privacy-preserving distributed artificial intelligence. His professional journey reflects a blend of strong teaching acumen, impactful research contributions, and a commitment to student mentorship. By integrating cutting-edge research with pedagogical innovation, he has significantly contributed to both the academic and practical realms of computer science. His scholarly work has been published in leading international journals, positioning him as an emerging voice in the domain of distributed and intelligent systems.
Education
Mr. Chaudhary holds a solid academic foundation in Computer Science and Engineering. He is currently pursuing a doctoral degree in the discipline from a reputed institute, with his research focusing on advancing clustered federated learning through nature-inspired optimization techniques and privacy-preserving mechanisms. Prior to his doctoral studies, he completed a Master of Technology in Computer Science and Engineering from a recognized university, where he specialized in developing optimization-based solutions for networked systems. His undergraduate studies in Computer Science and Engineering laid the groundwork for his expertise in networking, algorithms, and distributed computing. His academic trajectory has been characterized by consistent excellence, deep engagement with emerging technologies, and a passion for continuous learning.
Experience
Mr. Chaudhary has served as an Assistant Professor in the Department of Computer Science and Engineering at a well-regarded university, where he played a pivotal role in both academic delivery and extracurricular technical initiatives. He has taught core and advanced subjects including Computer Networks, Cryptography, Theory of Automata, and Operating Systems. Beyond classroom teaching, he has supervised multiple student research projects in clustered federated learning, wireless sensor networks, and optimization algorithms, guiding them toward impactful academic outputs. He has also organized workshops, industrial training programs, and technical seminars to bridge the gap between academic knowledge and industry expectations. Recognized for his commitment to institutional development, he has twice received the Best Training Coordinator award for his exceptional coordination of student training and placement activities.
Research Interest
Mr. Chaudhary’s research interests encompass a range of advanced computing topics, with a central focus on clustered federated learning (CFL) — an emerging paradigm in distributed artificial intelligence. He is particularly interested in integrating nature-inspired optimization algorithms, such as grey wolf optimization, real-coded genetic algorithms, and artificial bee colony optimization, to enhance the performance, scalability, and efficiency of CFL systems. His work also delves into privacy-preserving machine learning frameworks, aiming to ensure data security while maintaining high accuracy in distributed environments. Additionally, his research contributions to wireless sensor networks include the development and optimization of energy-efficient communication protocols, making significant strides toward sustainable IoT deployments. His scholarly output in reputed IEEE and Springer journals reflects both depth and breadth in these domains, combining theoretical rigor with practical applicability.
Awards
In recognition of his professional excellence, Mr. Chaudhary has been honored with multiple accolades. He was awarded the Best Training Coordinator on two occasions for his outstanding contributions to student training programs. His postgraduate studies were marked by top academic performance, earning him institutional recognition for his achievements. He has also been acknowledged for his contributions to sports, having represented his district in senior state-level football championships. Furthermore, his research dissemination activities, including paper presentations at national conferences, highlight his active engagement with the wider academic community.
Publications
Review paper on energy-efficient protocols in wireless sensor networks
Authors: R Chaudhary, S Vatta, P No
Journal: IOSR Journal of Engineering 4 (02), 01-07
Year: 2014
A tutorial of routing protocols in wireless sensor networks
Authors: R Chaudhary, DS Vatta
Journal: International Journal of Computer Science and Mobile Computing 3 (6), 971-979
Year: 2014
Performance optimization of WSN using deterministic energy efficient clustering protocol: A review
Authors: R Chaudhary, S Vatta
Journal: International Organization of Scientific Research Journal of Engineering 4 …
Year: 2014
A systematic review on federated learning system: a new paradigm to machine learning
Authors: RK Chaudhary, R Kumar, N Saxena
Journal: Knowledge and Information Systems 67 (2), 1811-1914
Year: 2025
Enhancing clustered federated learning using artificial bee colony optimization algorithm for consumer IoT devices
Authors: RK Chaudhary, R Kumar, K Aurangzeb, J Bedi, MS Anwar, A Choi
Journal: IEEE Transactions on Consumer Electronics
Year: 2024
Ameliorating clustered federated learning using real-coded genetic algorithm
Authors: RK Chaudhary, R Kumar, N Saxena
Journal: Cluster Computing 28 (6), 1-18
Year: 2025
Ameliorating clustered federated learning using grey wolf optimization algorithm for healthcare IoT devices
Authors: RK Chaudhary, R Kumar, N Saxena
Journal: The Journal of Supercomputing 81 (8), 976
Year: 2025
Conclusion
Mr. Rajesh Kumar Chaudhary exemplifies the qualities of a committed educator, innovative researcher, and proactive mentor. His expertise in clustered federated learning, optimization algorithms, and privacy-preserving distributed AI positions him at the forefront of technological advancements in distributed computing systems. With a career that blends academic excellence, impactful research, and active student engagement, he continues to contribute to the development of intelligent, secure, and efficient computing frameworks. His vision aligns with the future of intelligent systems, where collaborative machine learning and optimization strategies will play a crucial role in shaping secure and high-performance applications across domains.