{"id":735,"date":"2026-10-06T06:47:40","date_gmt":"2026-10-06T06:47:40","guid":{"rendered":"https:\/\/csnet-conference.org\/2026\/?page_id=735"},"modified":"2026-10-06T07:06:02","modified_gmt":"2026-10-06T07:06:02","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/csnet-conference.org\/2026\/tutorials\/","title":{"rendered":"Tutorials"},"content":{"rendered":"\n

Tutorials <\/mark><\/strong><\/h2>\n\n\n\n

Tutorial #1<\/mark><\/strong><\/h4>\n\n\n\n

Title: Resilient and Resource-Efficient Federated Learning: Algorithms, Attacks, Monitoring, and Sustainable Deployment<\/em><\/strong><\/p>\n\n\n\n

Abstract: <\/strong>Federated Learning (FL) enables multiple organizations and edge devices to collaboratively train machine learning models while retaining data at its source. However, data locality does not automatically ensure that an FL system is resilient, secure, or resource-efficient. Practical deployments must address non-independent and identically distributed data, heterogeneous client resources, unreliable networks, malicious participants, and the computational and environmental costs of distributed training.
The aim of this tutorial is to provide a systems-oriented introduction to the foundations and practical challenges of FL. It first presents the standard training workflow and core algorithms, before examining how data, resource, and network heterogeneity affect model convergence and training performance. The tutorial then introduces major attack vectors, including poisoning, backdoor, Sybil, model-inversion, membership-inference, and model-extraction attacks. Particular attention is given to the role of benchmarking and multi-level monitoring in identifying bottlenecks, comparing configurations, detecting abnormal behavior, and supporting resilient and resource-efficient FL operation. Drawing on practical experience and research work from the University of Nicosia Artificial Intelligence Laboratory (UNIC AILab) in regard to FL experimentation and monitoring frameworks, the tutorial also discusses reproducible evaluation, infrastructure-aware optimization, energy consumption, and carbon-aware scheduling.
With the conclusion of the tutorial, participants will leave with a structured understanding of how to design, evaluate, monitor, and secure resilient and resource-efficient FL systems across the Edge-Cloud continuum.<\/p>\n\n\n

\"Demetris

Demetris Trihinas<\/b>
(University of Cyprus, Cyprus)<\/p><\/div><\/div>\n\n\n\n

Bio: Dr. Demetris Trihinas<\/strong> is a tenured Assistant Professor in the Department of Computer Science at the University of Nicosia (UNIC), with specialization in Big Data Management and Processing. He is also a Senior Member of the University\u2019s Artificial Intelligence Lab (AILab) leading initiatives in Data-Intensive Computing and Machine Learning Operations (MLOps). As of February 2025, he serves as the Program Coordinator for the BSc in Data Science program at the University of Nicosia. Dr. Trihinas research focuses on designing scalable and adaptive data analytics and AI systems by exploring the intersection between Big Data Management, Distributed Systems, and Machine Learning. Examples of his research work include performance and quality-aware optimization of data streaming applications, scalable emulation of data-intensive IoT services, benchmarking geo-distributed ML applications, low-cost and self-adaptive techniques for DNN inference, and Federated Learning. His current research agenda focuses on energy-aware MLOps and latency-efficient DNN inference, areas that are increasingly critical for establishing sustainable and responsible AI practices. Dr. Trihinas work is published in IEEE\/ACM journals and conferences such as Trans. On Services Computing (TSC), Trans. on Cloud Computing (TCC), Internet Computing, INFOCOM, ICDCS, BigData, EuroSys, CCGrid, SEC, UCC, CloudCom, IC2E and IoTDi. Dr. Trihinas has extensive experience in European and nationally co-funded research projects, including roles as Project Coordinator, Scientific Coordinator, and Work Package Leader. As of September 2026, he is the Project Coordinator for the EU-funded LLMs4FL project that targets AI-assisted prototyping and token economics for Federated Learning systems.<\/p>\n\n\n\n

<\/p>\n\n\n

\"Moysis

Moysis Symeonides<\/b>
(University of Cyprus, Cyprus)<\/p><\/div><\/div>\n\n\n\n

Bio:<\/strong> Dr. Moysis Symeonides<\/strong> is a Senior Researcher at the Laboratory for Internet Computing (LInC) at the University of Cyprus. He received his PhD in Computer Science from the University of Cyprus in 2022, focusing on the modelling and experimental evaluation of data-intensive applications across the Edge-to-Cloud continuum. His academic background also includes an MSc in Information Systems from Aristotle University of Thessaloniki and a BSc in Computer Science and Biomedical Informatics from the University of Thessaly. Moreover, he has more than a decade of research and engineering experience spanning distributed systems, cloud and edge computing, IoT, data analytics, and AI systems. His current research focuses on sustainable and energy-efficient AI, LLM systems, federated learning, geo-distributed computing, and next-generation cloud-edge infrastructures. He has participated in numerous EU research projects under FP7, Horizon 2020, and Horizon Europe, contributing as a researcher, technical coordinator, and proposal author, and has authored more than 30 peer-reviewed publications. His research has received several distinctions, including Best Paper Awards at IEEE\/ACM UCC 2025, IEEE CloudCom 2023, IEEE\/ACM UCC 2023, and ACM\/IEEE IoTDI 2022, the Best Student Paper Award at IEEE ISCC 2022, and the Best Demo Award at ACM\/IEEE SEC 2020.<\/p>\n\n\n\n

\n\n\n\n

Tutorial #2<\/mark><\/strong><\/h3>\n\n\n\n

Title: Introduction to Test Driven Development (TDD)<\/em><\/strong><\/p>\n\n\n\n

Abstract:<\/strong> Test-Driven Development (TDD) is an approach in which automated tests are written before production code. This tutorial introduces the red, green, refactor cycle: write a failing test, implement code to make it pass, and improve the code safely. Participants will practice translating requirements into unit tests and developing small Java features incrementally using JUnit.<\/p>\n\n\n\n

The session is hands-on, and participants should bring a laptop with the required software installed. Some programming experience is recommended, with Java preferred. Experience in C, C++, Python, or a similar language is also suitable; participants without programming experience are welcome to attend as observers.<\/p>\n\n\n

\"Harald

Harald Gjermundrod<\/b>
(University of Nicosia, Cyprus)<\/p><\/div><\/div>\n\n\n\n

Bio:<\/strong> Harald Gjermundr\u00f8d<\/strong> is currently a Professor of Computer Science in the Department of Computer Science at the University of Nicosia, which he joined in September 2008. Prior to this, he was a post-doctorate associate (2006-2008) at the High-Performance Computing Systems Laboratory in the Computer Science Department of the University of Cyprus.<\/p>\n\n\n\n

Gjermundr\u00f8d received his PhD, MS, and BS degrees in computer science from Washington State University in 2006, 2001, 1999 respectively and Dipl.-Ing degree from Oslo Metropolitan University in 1998. His research interests include Distributed Computing Systems, Computer Security, Cyber Security, Data Protection, and Blockchain Technology. He is a co-director of the Informatics Security Laboratory at University of Nicosia that is conducting research on active defense and data protection.<\/p>\n\n\n\n

Gjermundr\u00f8d has worked on projects funded by the EU, the National Institute of Technology(US), and the National Science Foundation(US). During his involvement in research projects, he heavily assisted in the development of various software products, including GridStat (middleware product critical infrastructures), ICGrid (storage and sharing of medical data using Grid infrastructure), g-Eclipse (Eclipse technology project for using, developing, and managing Grid infrastructure), and ReProTool (tool for assisting Universities in program development using Learning Outcomes and ECTS). In addition, he has supervised students (at the undergraduate and graduate level) who developed prototype software like MapQFTool, NoteLocker, HoneyCY, and privacyTracker.<\/p>\n\n\n\n

Gjermundr\u00f8d has co-authored more than 50 peer reviewed scientific publications, served on the program committee for more than 70 scientific conferences and has been a reviewer for several scientific journals. He has also given conference keynote presentations, served on conference panels, served as conference session chair, and moderated panel. He was elevated to committer status for the Eclipse open source project, and is currently listed as an Eclipse alumni. He is also a senior member of ACM and was the chair of the ACM Cyprus chapter from 2015 until 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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