دکتر بهروز شاه قلی قهفرخی

استاد

گروه مهندسی فناوری‌اطلاعات
دانشکده مهندسی کامپیوتر

پست الکترونیکی

shahgholi@eng.ui.ac.ir

آدرس

دانشگاه اصفهان میدان آزادی اصفهان، ایران
کد پستی : 8174673441

2009

2025

فعالیت های پژوهشی بر اساس سال

معرفی کلی

علاقه مندی پژوهشی

Wireless, Cellular and Ad-hoc Networks Internet of Things Computer and Broadband Networks, Internet Engineering Network Security

فعالیت‌های حرفه‌ای

5G/6G Networks Internet of Things Edge/Fog Computing Quantum Communications Network Security

دروس

مباحث پيشرفته در شبکه هاي کامپيوتري - طراحی و اتوماسیون شبکه مباحث پيشرفته در شبکه هاي کامپيوتري - اينترنت اشياء مديريت شبکه هاي مخابراتي و کامپيوتري شبکه هاي بي سيم و سيار امنيت شبکه هاي کامپيوتري مهندسي اینترنت شبکه هاي کامپيوتري طراحي الگوريتم ساختمان داده ها برنامه نویسی پیشرفته

برونداد‌های پژوهشی

همه موارد

مقالات

2006

2026

مقالات لاتین

2026

Deep reinforcement learning based energy management in full-duplex ultra dense networks with cell switching and radio resource allocation

( Article )

Rahmati, T., Shahgholi, B.

Publication Date: 2026

Computer Communications (1873703X)248

2025

Channel-hopping sequence generation for blind rendezvous in cognitive radio-enabled internet of vehicles: A multi-agent twin delayed deep deterministic policy gradient-based method

( Article )

Asadi vasfi, M., Shahgholi, B.

Publication Date: 2025

Computer Communications (1873703X)243

Mobile crowd sensing based spectrum monitoring with privacy protection and malicious behavior detection using hyperledger fabric and identity mixer

( Article )

Yaqoubi, Z., Shahgholi, B., Seifhashemi, F., Mahdavi, M.

Publication Date: 2025

Journal of Supercomputing (15730484)81(7)

On the local convergence of ADAM-DPGAN with simultaneous and alternating gradient decent training methods

( Article )

Azadmanesh, M., Shahgholi, B., Ashouri, M.

Publication Date: 2025

Expert Systems with Applications (0957-4174)262

Generative Adversarial Networks (GANs) do not ensure the privacy of the training datasets and may memorize sensitive details. To maintain privacy of data during inference, various privacy-preserving GAN mechanisms have been proposed. Despite the different approaches and their characteristics, advantages, and disadvantages, there is a lack of a systematic review on them. This paper first presents a comprehensive survey on privacy-preserving mechanisms and offers a taxonomy based on their characteristics. The survey reveals that many of these mechanisms modify the GAN learning algorithm to enhance privacy, highlighting the need for theoretical and empirical analysis of the impact of these modifications on GAN convergence. Among the surveyed methods, ADAM-DPGAN is a promising approach that ensures differential privacy in GANs for both the discriminator and the generator networks when using the ADAM optimizer, by introducing appropriate noise based on the global sensitivity of discriminator parameters. Therefore, this paper conducts a theoretical and empirical analysis of the convergence of ADAM-DPGAN. In the presented theoretical analysis, assuming that simultaneous/alternating gradient descent method with ADAM optimizer converges locally to a fixed point and its operator is L-Lipschitz with L < 1, the effect of ADAM-DPGAN-based noise disturbance on local convergence is investigated and an upper bound for the convergence rate is provided. The analysis highlights the significant impact of differential privacy parameters, the number of training iterations, the discriminator's learning rate, and the ADAM hyper-parameters on the convergence rate. The theoretical analysis is further validated through empirical analysis. Both theoretical and empirical analyses reveal that a stronger privacy guarantee leads to a slower convergence, highlighting the trade-off between privacy and performance. The findings also indicate that there exists an optimal value for the number of training iterations regarding the privacy needs. The optimal settings for each parameter are calculated and outlined in the paper. © 2024 Elsevier Ltd

RAIDAD: A model-driven framework for automated and agile development of IoT data analysis software

( Article )

Gholami, M., Zamani, B., Shahgholi, B.

Publication Date: 2025

Information and Software Technology (09505849)187