A Hybrid Event Technically Co-Sponsored by IEEE Austrian Section. Co-Located with the 3rd International Conference on Foundation and Large Language Models (FLLM2025)
Four days of cutting-edge research
Historic city of innovation
October 1, 2025
The Federated Learning and Intelligent Computing Systems (FLICS) symposium brings together researchers, practitioners, and industry leaders to explore the convergence of federated learning with intelligent computing systems, edge AI, and autonomous workflows.
As we advance toward 6G networks, pervasive edge intelligence, and decentralized cyber-physical systems, the need for collaborative, privacy-preserving learning approaches has never been more critical.
Our Symposium focuses on the intersection of federated learning systems with emerging intelligent computing paradigms, including agentic AI workflows, edge intelligence, digital twin technologies, mobile computing, and distributed machine learning.
We aim to address the fundamental challenges of engineering and deploying scalable, secure, and efficient federated learning systems across diverse computational environments in various application domains, including health, energy management, industrial automation, and smart cities.
FLICS 2025 provides a unique platform for interdisciplinary collaboration, bridging theoretical foundations and practical implementations. The symposium welcomes contributions from both researchers and practitioners in the field of FL.
Learn MoreDistributed learning architectures for edge computing environments, orchestration, reliability, and deployments at the edge.
Autonomous systems and multi-agent collaboration frameworks
Differential privacy,Secure computation, Secure aggregation and trust frameworks.
5G/6G networks and mobile edge computing solutions
Industry 4.0, Healthcare, energy, smart cities and more practical implementation.
Next-generation computing paradigms, research frontiers, Digital twins, and self-improving systems
Be part of the leading conference shaping the future of intelligent computing and federated learning systems.