Prof. Danilo Pelusi
University of Teramo, Italy
Brief biography:
Danilo Pelusi received his degree in Physics from the University of Bologna, Italy, and his Ph.D. in Computational Astrophysics from the University of Teramo, Italy. He is currently a Professor of Computer Science in the Department of Communication Sciences at the University of Teramo. He has served as Editor for Springer, Elsevier, and CRC Press books, and as Associate Editor of several leading IEEE journals, including IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, and IEEE Transactions on Fuzzy Systems. He has also acted as Guest Editor for numerous IEEE, Elsevier, and Springer journals. A frequent keynote speaker, Guest of Honor, and chair of IEEE conferences, he is the inventor of international patents in Artificial Intelligence. He has been recognized among the world’s top 2% of scientists from 2021 to 2024 and ranked in the top 0.5% of scholars worldwide in the 2024 ScholarGPS rankings. His research interests include fuzzy logic, neural networks, information theory, machine learning, and evolutionary algorithms.
Talk Title:
Evolutionary Optimization for Efficient and Privacy-Preserving Federated Learning
Abstract:
Federated Learning (FL) has emerged as a transformative paradigm for privacy-preserving distributed intelligence, enabling multiple participants to collaboratively train machine-learning models without sharing their local data. Despite its potential, the widespread adoption of FL remains limited by substantial communication costs and the challenges associated with heterogeneous, non-independent and identically distributed (non-IID) data. This keynote will present a novel evolutionary optimization framework designed to enhance the efficiency of federated systems by jointly exploiting sparse neural architectures and multi-objective optimization. The proposed perspective addresses the trade-offs among model performance, communication efficiency, and architectural complexity, providing new insights into the development of scalable, resource-efficient, and privacy-aware federated learning solutions.
Prof. Mike Yuliana
Politeknik Elektronika Negeri Surabaya (PENS), Indonesia
2027 IEEE Indonesia Section Chair-Elect
Brief Biography:
Prof. Mike Yuliana is a Professor at Politeknik Elektronika Negeri Surabaya (PENS), Indonesia. Her research interests include wireless communications, physical-layer security, secret key generation, Internet of Things (IoT), and intelligent systems. Her recent research focuses on wireless channel-based secret key generation, particularly exploiting channel reciprocity and signal processing techniques to enhance secure communication in dynamic wireless environments, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and IoT networks. She is also actively engaged in developing AI and IoT-based solutions for smart agriculture and intelligent transportation. Her research emphasizes the development of efficient, secure, and practical technologies for next-generation wireless and mobile networks.
Keynote Title:
From Wireless Signals to Secret Keys: Advancing Physical-Layer Security for Mobile and IoT Networks
Abstract:
The rapid growth of wireless and mobile networks, particularly in Internet of Things (IoT) and intelligent transportation systems, has introduced new challenges in establishing secure and efficient communication. Physical-layer Secret Key Generation (SKG) offers a promising approach by exploiting the inherent randomness and reciprocity of wireless channels to establish shared cryptographic keys without directly exchanging the secret keys.
This keynote explores recent advances and practical challenges in wireless channel-based secret key generation, with particular emphasis on Received Signal Strength Indicator (RSSI)-based techniques. It highlights the role of signal processing, adaptive quantization, information reconciliation, and privacy amplification in improving key agreement reliability and security under dynamic wireless conditions.
Drawing on research involving Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and IoT communication scenarios, the discussion addresses key challenges related to mobility, channel fluctuations, key disagreement, randomness, and computational efficiency. Finally, the keynote outlines future research directions toward adaptive, lightweight, and mobility-aware key generation, emphasizing the transition from theoretical concepts to practical implementations for secure next-generation wireless and mobile networks.
Prof. Ir. Ni Made Ary Esta Dewi Wirastuti, ST, M.Sc, Ph.D., IPU., ASEAN Eng.
Universitas Udayana, Indonesia
Brief Biography:
Ni Made Ary Esta Dewi Wirastuti, she received the B.Eng. degree in electrical engineering from Udayana University, Bali, Indonesia, in 2000, the M.Sc. degree in mobile communication systems from University of Surrey, Guildford, United Kingdom, in 2002 and the Ph.D. degree in Telecommunication Systems from University of Bradford, West Yorkshire, United Kingdom, in 2007. From 2007 to 2009, she was a Post Doctoral Fellowship with the Mobile and Satellite Communication Research Centre (MSCRC), University of Bradford, United Kingdom worked to the VeSeL (Village e-Science for Life) project, Engineering and Physical Sciences Research Council (EPSRC) grant. The profession of Engineer (Ir.) from Udayana University and the Associate Professional Engineer Certification from PII (Indonesian Engineers Association) in 2020. Her research interest includes the development of physical layer model for the next wireless and mobile communication systems, Wireless technology. She has been a lecturer in Department of Electrical Engineering, Faculty of Engineering at Udayana University, Bali, Indonesia, since 2001. (Email: dewi.wirastuti@unud.ac.id; https://orcid.org/0000-0001-5522-8684)
Title:
Reimagining 6G: From Signal Processing to Intelligent Networks
Abstract:
The evolution toward sixth-generation (6G) wireless networks requires advances beyond higher data rates toward intelligent, reliable, and energy-efficient connectivity. Achieving this vision relies on innovations in physical-layer signal processing, waveform design, and adaptive network optimization. This keynote explores computationally efficient signal processing through Very Fast Fourier Transform (VFFT)-based Orthogonal Frequency-Division Multiplexing (OFDM) as a potential approach for next-generation wireless systems. By replacing conventional inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) operations with inverse VFFT (IVFFT) and VFFT implementations, different G-OFDM configurations offer opportunities to reduce computational complexity. Analytical and simulation studies under additive white Gaussian noise (AWGN) and Rayleigh fading channels examine bit error rate (BER), peak-to-average power ratio (PAPR), and sensitivity to phase noise and carrier frequency offset. The findings highlight the trade-offs between implementation complexity and communication performance, particularly those associated with non-uniform signal-to-noise ratios across subcarriers. Building on these findings, the keynote discusses future research opportunities in adaptive signal processing and AI-assisted optimization. It further explores how physical-layer innovations can support reliable and energy-efficient connectivity for emerging Internet of Things (IoT) applications and contribute to the development of intelligent 6G networks.
Keywords: 6G networks, Internet of Things, signal processing, OFDM, VFFT-based OFDM, computational complexity, artificial intelligence.