Amir Ziaeddini - ECE PhD Student of the Month - March 2025
Amir Ziaeddini is currently a Ph.D. student at NJIT, advised by Professor Joerg Kliewer, focusing on privacy-preserving techniques in decentralized federated learning networks. His research interests include wireless communications, signal processing, and machine learning. Before that, from 2012 to 2023, he worked in industry as a researcher in an R&D center and as a Radio Access Network (RAN) planning engineer at MCI, a mobile operator in Iran. From 2020 to 2022, he served as a lecturer at Shamsipour Technical College in Iran, teaching courses such as Modern Communication Networks and Wireless Mobile Networks. Furthermore, he earned a M.Sc. in Electrical Engineering from Isfahan University of Technology, Iran in 2012, and a B.Sc. in Electrical Engineering from the University of Mazandaran, Iran in 2009.
What would you say that could be the next big thing in your area of research?
The next big advancement in my research area is enhancing privacy-preserving decentralized federated learning (DFL) for secure, efficient model training across distributed devices. Key directions include reducing communication overhead, improving differential privacy mechanisms, and adapting to heterogeneous edge devices with non-IID data distributions. Reinforcement learning and blockchain-integrated federated learning can enhance collaboration and security. Also, energy-efficient algorithms will make DFL viable for resource-constrained environments. These advancements will drive the development of decentralized AI solutions that balance privacy, scalability, and performance.
Getting started on PhD research, especially when advanced theories and mathematics are involved can be very challenging. How did you manage over the first two years of your PhD study?
The first two years of my PhD were challenging, especially with the complexity of advanced theories and mathematical foundations in decentralized learning. To navigate this, I dedicated significant time to studying key research papers and textbooks, particularly focusing on proofs of convergence in decentralized learning systems. Discussions with Professor Kliewer and his postdoctoral research associate, Dr. Yauhen Yakimenka, have significantly enhanced my understanding and shaped my research approach. I also conducted small-scale simulations to bridge the gap between theory and implementation. By continuously refining my problem-solving approach and integrating feedback, I gradually developed a deeper intuition for the field and gained confidence in tackling complex questions.
Have you had experience being inspired by others in a causal conversation and coming up with new ideas for research or coursework? Please briefly describe the experience.
Casual conversations with my lab mates have often helped me generate new ideas, especially when troubleshooting errors in my simulations. Their insights have guided me toward alternative solutions and more efficient debugging strategies. Additionally, discussions with friends working in other fields of electrical and computer engineering have introduced me to novel approaches and optimization techniques. In particular, I have learned about simulation methods that improve processing speed and reduce computational complexity. These exchanges have broadened my perspective and helped me refine my research methods.