KFUPM · Dhahran, Saudi Arabia

Anis Elgabli

Anis Elgabli
IEEE Senior Member · Ph.D. Purdue University
Assistant Professor — Industrial & Systems Engineering / IRC-CSS

My research lies at the intersection of machine learning, communication networks, and optimization. I develop scalable and privacy-preserving intelligent systems, with a particular focus on distributed learning, wireless networks, edge intelligence, and data-driven mobility and logistics.

1500+ Citations
20 h-index
5+ Active Projects
2 Patents
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SERVER MODEL CLIENT DATA-1 CLIENT DATA-2 CLIENT DATA-3 CLIENT DATA-4 CLIENT DATA-5 🔒 🔒 🔒 🔒 🔒 Federated Learning — Privacy-Preserving Distributed Training
Focus Areas
Research Interests

Distributed AI & Federated Learning

Advancing distributed and federated intelligence through scalable learning, optimization, and collaborative decision-making across decentralized systems. Research focuses on efficiency, robustness, and learning under practical resource and network constraints.

Trustworthy & Privacy-Preserving AI

Developing secure, reliable, and privacy-aware federated learning systems that enable collaborative training while protecting sensitive data. Research spans trustworthy AI, privacy-enhancing technologies, robustness, fairness, and secure computation.

AI for Communication Networks

Integrating artificial intelligence into next-generation communication systems to enable adaptive, efficient, and autonomous network operation. Research includes wireless optimization, edge intelligence, and future network architectures.

Video Streaming & Media Delivery

Designing intelligent systems for adaptive video streaming, cooperative content delivery, and quality enhancement over heterogeneous networks. Research spans streaming optimization, video super-resolution, bandwidth-aware delivery, and user experience.

Mobility, Logistics & Socio-Technical Systems

Developing data-driven and AI-powered frameworks for intelligent transportation, delivery, and mobility ecosystems. Research focuses on user behavior modeling, demand prediction, resource allocation, incentive design, and sustainable logistics operations.

AI for Healthcare, Smart Cities & Critical Infrastructure

Applying artificial intelligence and distributed learning to address real-world challenges in healthcare, urban systems, energy networks, and critical infrastructure. Research emphasizes scalable, privacy-aware, and human-centered solutions for societal impact.

Selected Works
Publications
2025
PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning
IEEE Open Journal of the Communications Society
Federated Learning Privacy ADMM
2024
Quantized FedPD (QFedPD): Beyond Conventional Wisdom — The Energy Benefits of Frequent Communication
IEEE Internet of Things Journal
Federated Learning IoT Quantization
2022
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
International Conference on Machine Learning (ICML)
ICML Federated Learning Privacy
2021
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
IEEE Transactions on Communications
Federated Learning Wireless
2020
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Journal of Machine Learning Research (JMLR), 21(76), 1–39
JMLR ADMM Distributed ML
2020
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
IEEE Transactions on Communications, 69(1), 164–181
Decentralized ML Quantization
2019
Reinforcement Learning Based Vehicle-Cell Association Algorithm for Highly Mobile Millimeter Wave Communication
IEEE Transactions on Cognitive Communications and Networking, 5(4)
Reinforcement Learning mmWave V2X
2019
A Low-Complexity Detection Algorithm for Uplink Massive MIMO Systems Based on Alternating Minimization
IEEE Wireless Communications Letters, 8(3), 917–920
Massive MIMO Signal Detection
2019
SmartStreamer: Preference-Aware Multipath Video Streaming over MPTCP
IEEE Transactions on Vehicular Technology, 68(7)
Video Streaming MPTCP
2019
GroupCast: Preference-Aware Cooperative Video Streaming with Scalable Video Coding
IEEE/ACM Transactions on Networking, 27(3), 1138–1150
Video Streaming SVC
2018
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
IEEE/ACM Transactions on Networking, 26(4), 1633–1645
Video Streaming Adaptive Bitrate
View Full Publication List on Google Scholar →
Active Research
Ongoing Projects
Funded — KFUPM EC241028

Privacy-Preserving and Communication-Efficient Federated Learning

Developing novel ADMM-based frameworks that simultaneously achieve differential privacy guarantees and minimize communication overhead in federated networks. Includes quantized gradient methods and perturbation strategies.

Federated Learning Privacy ADMM
Funded — KFUPM INCS2502

Learn Efficiently Without a Server (Decentralized FL)

Eliminating the central server bottleneck in federated learning. Designing fully decentralized protocols over graph topologies that maintain convergence guarantees while reducing communication and single-point failure risks.

Decentralized ML Graph Topology Convergence analysis
Funded — KFUPM IN26019

Personalized Federated Video Super-Resolution

Developing a privacy-preserving edge video streaming framework that delivers HD/4K content by enhancing low-resolution video directly on user devices. The project combines personalized federated learning, differential privacy, and AI-based super-resolution to reduce bandwidth consumption, optimize edge caching, and improve streaming quality.

Video Streaming Federated Learning Edge Intelligence Video Super-Resolution Differential Privacy
International Collaboration — KFUPM & Northern Research (Norway)

PulseVAD: Intelligent Control and Monitoring for Ventricular Assist Devices

Developing adaptive sensing, communication, and control technologies for next-generation pulsatile ventricular assist devices (VADs). The project integrates multimodal physiological sensing, energy-aware telemetry, and real-time data processing to enable personalized and reliable support for patients with heart failure.

Digital Health Medical IoT Adaptive Control Physiological Sensing Embedded Systems
KFUPM
Teaching
ISE 504
Optimization Methods for Data Science
Spring 2025
ISE 291
Introduction to Data Science
Fall 2023 · Spring 2024 · Fall 2024 · Spring 2025· Spring 2026
ISE 468
Introduction to Data Analytics & Machine Learning
Fall 2023 · Fall 2024· Fall 2025
ISE 487
Predictive Analytics Techniques
Spring 2024 · Spring 2025· Spring 2026
Public lectures: ISE91: Introduction to Data science YouTube channel
Previous: Advanced course "Strategic Learning in Wireless Communications" at University of Oulu — selected lectures available on the Intelligent Connectivity and Networks YouTube channel
Get in Touch
Contact
Email
anis.elgabli@kfupm.edu.sa
Location
Industrial & Systems Engineering Dept.
KFUPM, Dhahran, Saudi Arabia
Phone
+966 534 890 311
Google Scholar
scholar.google.com — Anis Elgabli
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IRC-CSS

Interdisciplinary Research Center for Communication Systems and Sensing, KFUPM

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IEEE Senior Member

Member since 2023. Reviewer for IEEE-TCOM, IEEE-TWC, Globecom, ICASSP, ICC, WCNC