Dr Ahmed M. A. Sayed, PhD, MPhil, BSc, FHEA

Senior Lecturer (Associate Professor); Principal Investigator & Head of SAYED Systems Lab; Research Lead of CNCS; Director of MSc DS Prog.; Stu. Exp. Lead of PhD AAMSB Prog.; DERI Fellow

Centre: Centre for Networks, Communication and Systems

Email: ahmed.sayed@qmul.ac.uk
Room Number: PP 1.02, People's Palace, Mile End Campus
Website: https://sayed-sys-lab.github.io
X: @ahmedcs982

Profile
Teaching
Research
Publications
Supervision
Performance
Grants

Profile

Dr. Ahmed M. A. Sayed (aka. Ahmed M. Abdelmoniem) is a Senior Lecturer (Research & Teaching), the equivalent of Associate Professor, at the School of Electronic Engineering and Computer Science at Queen Mary University of London, UK. He leads the SAYED Systems Lab and works on various topics related to Distributed Systems, Systems for ML & ML for Systems, Dencentralise AI/ML, Federated Learning, Mulit-Agent and Agentic AI, IoT/Edge/Fog/Cloud Computing, Congestion Control, and Software-Defined Networking (SDN). He is also the Research Lead of the Centre of Networks, Communications & Systems (CNCS), Director of the MSc Data Science (DS) Programme, Student Experience Lead of the Doctoral Programme for Advanced AI for Multi-Modal Spatial Biology (AAMSB), and Fellow of the Digital Environment Research Institute (DERI).

In 2017, he earned a Ph.D. degree in Computer Science and Engineering under the supervision of Brahim Bensaou from the Hong Kong University of Science and Technology (HKUST) ([Ph.D. Thesis PDF]), where he worked to enhance the performance of TCP applications in Data Center Networks. He completed with Distinction both the B.Sc. and M.Sc. degrees (Coursework & Research) in Computer Science from Assiut University (AUN), Egypt, in 2007 and 2012, respectively.

Before joining QMUL, he was a research scientist at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, working with Marco Canini in the SANDS Lab on problems related to distributed ML systems. Before that, he worked as a Senior Researcher at Huawei's Future Network Research Lab on the design and architecture of Application-Driven Networking (ADN). He also previously held the position of Assistant Professor at Assiut University, Egypt.

His research spans inter-related disciplines of computer science and engineering with a focus on system design and optimization for machine learning systems (Training, inference, and fine-tuning efficiency, Distributed/Decentralised AI/ML, Federated Learning, Agentic AI), distributed systems (architecture design, performance analysis, resource allocation, algorithmic optimization), and computer networks (traffic engineering, congestion control, performance optimization, software-defined networking).

He is always looking for bright and talented students and researchers who are passionate about researching to study and solving real-world problems. If you find the above topics intriguing, please get in touch by dropping him an email or visiting his personal webpage for any announced opportunities

Teaching

ECS640U/ECS640A/ECS765P Big Data Processing

Big Data Processing covers the new large-scale programming models that allow to easily create algorithms that process massive amounts of information with a cluster of computer nodes. These platforms hide the complexity of coordinating complex parallel computations across the cooperating nodes, instead providing developers with a high-level programming model.

The module is based on the MapReduce programming model. Lectures explain how multiple data analysis algorithms can be expressed under this model, and executed automatically over clusters of machines. The module also covers the internal mechanisms that a MapReduce framework uses to coordinate and execute the job among the infrastructure. Finally, additional related topics in the area of Big Data, such as alternative large-scale processing platforms, NoSQL data stores, and Cloud Computing execution infrastructure are presented. In addition to the lectures, weekly lab sessions and coursework exercises present multiple applications where real-world datasets are analysed using platforms such as Hadoop.

ECS637U/ECS757P Digital Media and Social Network

Online social networks and digital media services such as Facebook, twitter, Flickr, YouTube are changing the way we interact with the Internet and receive our news, content and recommendations. In this module, you will be introduced to the concepts of measurement, analysis, usability and privacy aspects of OSNs.

The module will bring together a number of studies from different measurement studies on the topic, designs for new systems, and the directions that such networks are taking with the new digital media plans. You will develop a deep understanding and analysis approach to learning specifically about Social Media and its properties.

Undergraduate Teaching

ECS637U/ECS757P Digital Media and Social Network

ECS640U/ECS640A Big Data Processing

Postgraduate Teaching

ECS765P Big Data Processing

Research

Research Interests:

See Ahmed M.A. Sayed’s research profile pages including details of research interests, publications, and live grants.

Examples of research funding:

2024 - Now: QMUL - Principal Investigator of UKRI-EPSRC-funded New Investigator Award (NIA) project on Knowledge Delivery System for Machine Learning at Scale (KUber) - 652,000 GBP

2026 - Now: QMUL - Principal Investigator of UKRI-InnovateUK-funded CyberASAP Phase 2 Proof-of-Concept project on Decentralised Threat Intelligence and Orchestration (Fed-IDS) - 60,000 GBP

2026 - Now: QMUL - Co-PI of UKRI-BBSRC Doctoral Focal Award on Advanced AI for Multi-modal Spatial Biology - 3,066,246 GBP

2026 - 2026: QMUL - Principal Investigator of UKRI-InnovateUK-funded CyberASAP Phase 1 project on Decentralised Threat Intelligence and Orchestration (Fed-IDS) - 24,000 GBP

2025 - 2026: QMUL - Principal Investigator of UKRI-InnovateUK-funded ICURE Discover project on Scalable Marketplace for Knowledge Integration Between Decentralised AI-based Solutions (KStore) - 2,500 GBP

2025 - 2026: UKRI NCFS NetworkPlus Project - A Roadmap for Fair and Efficient Allocation of Federated Digital Research Infrastructure (FAIR-Compute) - 120,000 GBP

2025 - Now: QUML - Huawei Research UK/Germany/China - Server Energy-Efficiency Testing and Benchmarking, Short-Term Joint Project - 31,000 GBP

2022 - 2023: QMUL - UKRI-funded project on Moderation in Decentralised Social Networks (DSNmod) - 81,000 GBP

2022 - Now: HKUST - GRF-funded project on ML methods for Congestion Control in SDN-based Networks - 600,000 HKD

2021 - 2024: KAUST - Competitive Research Grant on Machine Learning Architecture for Task-based Information Transfer - 400,000 USD.

2013-2017 Hong Kong PhD Fellowship (HKPFS) award, HK Research Grants Council - 155,000 USD for 4 years + tuition fees and travel grants.

2013-2017 HKPFS research travel grant award.

2017 Student Participation Grant, Local Computer Networks (IEEE LCN), IEEE CompSoc.

2015 Travel Grant award, Global Communications (GlobeCom) conference, IEEE ComSoc.

2007 FYP sponsorship award, Ministry of Telecommunications, Egypt.

2003-2007 Undergraduate Distinction award, for TGA of 85%-above, Assiut University.

2003-2007 Dean’s Honors, TGA of 85%+, Faculty of Computers and Information, Assiut University.

Publications

Click HERE for Ahmed M.A. Sayed’s Google Scholar Full List of Publications

Sample of Selected Publications:

Black-Box Membership Inference Attacks Against Contrastive Learning Via Aggressive Data Augmentations:Published in IEEE Transactions on Dependable and Secure Computing (2026), co-authored with Zixiong Wang, Lishuai Hou, Gaoyang Liu, Jianfeng Lu, Desheng Wang, and Chen Wang.
Agentic AI in Healthcare 5.0: Reference Architecture, Applications, and Challenges: Published in IEEE Internet of Things Journal (2026), co-authored with Thippa Reddy Gadekallu et al.
Too much of a good thing: Algorithmic trading, price discovery, and the efficiency threshold in emerging equity markets: Published in Finance Research Letters, Elsevier (2026), co-authored with Giridhar Reddy Bojja, Loknath Sai Ambati, Venkata Vinay Anga, and Harsha Sammangi.
An Explainable Multimodal Framework for Cyclist Safety Perception in Mixed Traffic Environments:Published in Applied Sciences, MDPI (2026), co-authored with Chia-Yen Chiang, Meihui Wang, Yasmin Fathy, and Mona Jaber.
Federated Unlearning Activated Backdoor Attacks: Published in IEEE Transactions on Dependable and Secure Computing (2026), co-authored with J. Chen, W. Shi, C. Hu, J. Lu, and C. Wang.
Federated Learning for Edge Computing Enabled Artificial Intelligence of Things: A comprehensive survey:Published in Knowledge-Based Systems, Elsevier (2026), co-authored with Qilei Li, Mingliang Gao, Wenzhe Zhai, Wentai Wu, and Chen Wang.
SMOQKE-IDS: Sparse Mixture of Quantum Kolmogorov-Arnold Network Experts for FL-IDS in Edge-IIoT:Published in IEEE Open Journal of the Communications Society (2026), co-authored with Jyoti Prakash Sahoo, Binayak Kar, Yi-Leh Wu, and Dimitris Chatzopoulos.
AdRo-FL: Secure and Informed Client Selection for Federated Learning under Adversarial Aggregator:Published in IEEE Open Journal of the Computer Society (2026), co-authored with M. K. Hossain, W. Aljoby, A. Elgabli, and K. A. Harras.
Federated Primitive-Preserving Audio Transformers for Non-Identifiable Infant Cry Classification: Published in IEEE ACCESS, vol. 14 (2026), co-authored with G. Owino, B. S. Kasamani, and E. Wornyo.
UDMP: Unified Delay-Driven Multipath Protocol for AI Clusters: Published in ACM/IEEE Transactions on Networking (ToN) (2026), co-authored with Chengyuan Huang et al.
Choir-IDS: A federated learning framework for fidelity-calibrated explainable intrusion detection system for edge-IoT networks: Published in Information Fusion, Elsevier (2026), co-authored with Jyoti Prakash Sahoo, Binayak Kar, and Dimitris Chatzopoulos.
Knowledge Routing for Decentralized Learning: Published in IEEE Transactions on Intelligent Systems (2025), co-authored with Yuchen Zhao.
Mitigating malicious model fusion in federated learning via confidence-aware defense: Published in Information Fusion, Elsevier (2025), co-authored with Qilei Li, Pantelis Papageorgiou, Gaoyang Liu, Mingliang Gao, Linlin You, and Chen Wang.
Toward Collaborative Intelligence in Digital Twin-based Federated Deep Reinforcement Learning: Applications, Case Studies and Challenges: Published in IEEE Network (2026), co-authored with Ahmad Arsalan, Tariq Umer, Rana Asif Rehman, and Shahid Mumtaz.
Poisoning as a Post-Protection: Mitigating Membership Privacy Leakage From Gradient and Prediction of Federated Models: Published in IEEE Transactions on Dependable and Secure Computing (IEEE TDSC) (2025), co-authored with G. Liu, T. Xu, Y. Yang, C. Wang, and J. Liu.
Troubleshooting Programmable Data Planes via Real-Time Table Information Recording: Published in ACM/IEEE Transactions on Networking (ToN) (2025), co-authored with Chengyuan Huang et al.
Unlocking the power of 4G/5G mobile networks: An empirical dive into quality and energy efficiency in YouTube Edge services: Published in Computer Networks, vol. 267 (2025), co-authored with Peixuan Song, JunKyu Lee, and Lev Mukhanov.
Alleviating Congestion via Switch Design for Fair Buffer Allocation in Datacenters: Published in IEEE Transactions on Cloud Computing (TCC) (2024), co-authored with Brahim Bensaou.
MpScope: Enabling Multi-pipeline Monitoring Inside a Switch: Published in Elsevier Computer Networks(2024), co-authored with Chengyuan Huang et al.
Towards a Decentralized Collaborative Framework for Scalable Edge AI: Published in MDPI Future Internet(2024), co-authored with Mona Jaber, Ali Anwar, Yuchao Zhang, and Mingliang Gao.
FLAIR: A Fast and Low-Redundancy Failure Recovery Framework for Inter Data Center Network: Published in IEEE Transactions on Cloud Computing (TCC) (2024), co-authored with Yuchao Zhang et al.
Manipulating Pre-trained Encoder for Targeted Poisoning Attacks in Contrastive Learning: Published in IEEE Transactions on Information Forensics and Security (2024), co-authored with Jian Chen, Yuan Gao, Gaoyang Liu, and Chen Wang.
Enhancing TCP via Hysteresis Switching: Theoretical Analysis and Empirical Evaluation: Published in ACM/IEEE Transactions on Networking (TON) (2023), co-authored with Brahim Bensaou.
A Comprehensive Empirical Study of Heterogeneity in Federated Learning: Published in IEEE Internet of Things (IoT) Journal (2023), co-authored with Chen-yu Ho, Pantelis Papageorgiou, and Marco Canini.
T-RACKS: A Faster Recovery Mechanism for TCP in Data Center Networks: Published in ACM/IEEE Transactions on Networking (TON) (2021), co-authored with Brahim Bensaou.
ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs: Accepted in Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, co-authored with Songyuan Li and Shiqiang Wang.
FedORA: Federated Objective-Resolved Adaptation for Non-Stationary Environments: Published in ACM EdgeSys @ MobiSys (2026), co-authored with Wai Fong Tam and Songyuan Li.
Breaking the Boundary Barrier: Robust Model Fingerprinting via Unlearnable Examples in Model-Parameter Space: Published in ACM SIGKDD (2026), co-authored with Tianlong Xu, Zixiong Wang, Gaoyang Liu, Jian Chen, and Chen Wang.
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning: Published in IEEE World Congress on SERVICES / IEEE Edge Conference (2026) – Best Student Paper Award, co-authored with Amir Hossein Shahdadian, Mahdi Taheri, Samira Nazari, and Christian Herglotz.
Discovering Latent Knowledge Prototypes for Heterogeneous Federated Learning: Published in ECAI (2025), co-authored with Qilei Li.

Supervision

Sept 2026 - Now: Hesham A. Al-mansouri - pursuing PhD in Computer Science (Focus on evaluation of the ethical behaviour of LLM-based autonomous agents) - Funded by KISR of the Kuwait Government.

Sept 2026 - Now: Qiuying Li - pursuing PhD in Computer Science (Focus on distributed and resource-efficient LLM inference) - Funded by QMUL-CSC Studentship

June 2026 - Now: Qi Xin - visiting PhD student in Computer Science from BUPT, China (Focus on efficient distributed LLM training and inference and AI networking) - Funded by QMUL-CSC Studentship

June 2026 - Now: Shijia Xu - visiting MPhil student in Computer Science from Chongqing University, China (Focus on reliable and efficient language-model systems) - Funded by Chongqing Unviersity

Sept 2025 - Now: Xiaolong Jia - pursuing PhD in Computer Science (Focus on Structured Fine Tuning of Foundation Models) - Funded by QMUL-CSC Studentship

May 2025 - Now: Leon Tabaro - pursuing PhD in Computer Science (Focus on Towards Parameter Efficient Fine Tuning of Generative AI and LLMs) - Home-UK Self-Funded

Sept 2024 - Now: Herman Tam - pursuing PhD in Computer Science (Focus on KUber - Knowledge Delivery System for Machine Learning at Scale) - Funded by QMUL via Project's Flexible Research Fund

Apr 2024 - Now: Qilei Li - Post-Doctoral Research Assistant (PDRA) Leading work on KUber - Knowledge Delivery System for Machine Learning at Scale Project - Funded by UKRI/EPSRC

Sept 2023 - Now: Bradely Aldous - pursuing PhD in Computer Science (Focus on Accelerated Distributed Machine Learning Systems) - Funded by EPSRC AIM CDT

Sept 2021 - Now: Yemisi Oyelek - pursuing EngD in Computer Science (Focus on Mitigating Video Degradation over Networks via Digitial Twin) - Funded by the EPSRC-CDT for Data-Centric Engineering

--------------------------------------------- Past Supervision -----------------------------

2023 PhD - QMUL, Efficient Machine Learning on Decentralized Data - Funded by CONACyT/IPN/UABC/CIMAV/UDLAP initiative

2023 PhD - QMUL, Federated ML for enhancing security and privacy of IoT networks - Self-Funded

2022 Intern - QMUL, Energy-Aware Methods for Federated Learning on Battery-Powered Devices.

2021 MS/PhD - KAUST, Mitigating Device Heterogeneity in Federated Learning via Asynchronous Stale Updates.

2021 MS/PhD - KAUST, Prioritizing Participant Selection for Efficient Federated Learning.

2021 MS/PhD - KAUST, Identifying the Limits of Gradient Sparsification Methods for Distributed Machine Learning.

2020 Research Student Interns - KAUST, Study of Fairness and Bias in Federated Learning settings.

2020 Research Student Interns - KAUST, An Efficient compression technique to reduce Communication in Distributed Deep Learning.

2019 MS/PhD - KAUST, Survey and Empirical Analysis of Compressed Communication for Distributed Deep Learning.

2019 MS/PhD - KAUST, Theoretical and Empirical Analysis of Layerwise and Whole-Model Compressed Communication Methods in Distributed Machine Learning.

2019 Research Student Intern - KAUST, Energy-Efficiency of Hardware Offloading: Case- Study on Distributed Machine Learning.

2019 Research Student Intern - KAUST, Scaling Distributed Machine Learning with In-Network Aggregation using Smart NICs.

2019 Research Student Intern - KAUST, Accelerating Distributed Deep Learning with Adaptive Compression and Communication Scheduling.

2018 PhD Research Student Intern - Huawei Research, Leveraging Programmable Data Plane to Accelerate Distributed Applications.

2018 PhD Research Student Intern - Huawei Research, An Online Learning Multi-Path Selection Framework for Multi-path Transmission Protocols.

2018 Research Student Intern - Huawei Research, Implementation of an SDN-based Fast-Slow Control System to Realise an Operational Prototype of the Application-Driven Networking (ADN) Framework.

2007-2013 FYPs UG Students - Assiut University, Management System for controlling Wireless Access Points, HoneyPot Server Application, WiiMote Body Tracking & Robot Control System, Steganography Application to hide data in images and videos, Remote Desktop Control using Mobile Phones, Mobile Application in Traffic Service, Tourist Heaven a tourist social networking application and Egyptian tourism company web system.

Performance

Funding & Grants: Total research funding exceeds USD $4.8 million, highlighted by serving as Co-PI/Co-I for the UKRI-BBSRC Doctoral Focal Award on Advanced AI for Multi-modal Spatial Biology (£3,066,246), Principal Investigator (PI) for the UKRI-EPSRC New Investigator Award (KUber project, £650k), UKRI Innovate UK Fed-IDS grants (£84k total), Huawei Research funding (£31k), .., etc.
Research Publications & Metrics: Author of over 160 publications in top-tier venues (including NeurIPS, ICLR, AAAI, EuroSys, INFOCOM, ICDCS, KDD, CCS, MLSys, ToN, and IoTJ), achieving an H-index >= 30, an I-index >= 57, and $\ge2900$ citations.
Leadership & Roles: Senior Lecturer (Associate Professor) and MSc Data Science Programme Director and Student Experience Lead of the Doctoral Programme for Advanced AI for Multi-Modal Spatial Biology (AAMSB)at the School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL).
Research Groups & Centers: Founder and head of the SAYED Systems Lab and Research Lead for QMUL's Networks, Communication, and Systems Research Centre (CNCS).
Awards & Recognition: Recipient of multiple honors, including Best Student Paper Awards at the IEEE Edge Conference (2026) and the IJCAI Federated Learning Workshop (2024), alongside the Hong Kong PhD Fellowship (HKPFS).
Professional Memberships: Senior Member of IEEE and IEEE ComSoc, alongside professional memberships in ACM and USENIX.

Grants