CFP: The 2025 Symposium on Federated Learning and Intelligent Computing Systems (FLICS 2025)
A Hybrid Event
Technically Co-Sponsored by IEEE Austrian Section
https://intelligent-systems.net/flics/

Co-Located with the 3rd International Conference on Foundation and Large Language Models (FLLM2025)
Theme: Federated Learning and Its Applications
[Vienna, Austria] — [25-28 November, 2025]

Scope
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 conference 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.

Topics of Interest
We invite submissions addressing, but not limited to, the following areas:
Federated Learning Systems & Edge Intelligence
Challenges of FL systems deployment in production environments
FL systems automation and self-tuning capabilities
Scalable federated learning architectures for large-scale deployments
Cross-silo and cross-device federated learning systems
Hardware-aware and resource-efficient federated learning
Communication-efficient FL (quantization, sparsification, compression techniques)
FL under client mobility, heterogeneity, and intermittent connectivity
Network-aware optimization and system-level co-design for FL
Benchmark and evaluation frameworks for FL systems in mobile/wireless environments
FL deployment in UAVs, mobile edge clouds, and autonomous systems
Agentic Workflows and Collaborative AI
Federated learning for agentic AI systems and autonomous workflows
Collaborative learning in multi-agent environments
Privacy-preserving agent-to-agent communication and coordination
Federated training of foundation models for agentic applications
Distributed learning for tool-use optimization and workflow adaptation
User-agent interaction personalization through federated approaches
Privacy, Security, and Trust
Privacy-enhancing technologies for federated learning
Secure aggregation protocols and cryptographic methods
Trustworthy and explainable federated learning systems
Resilient and robust FL systems against attacks
Privacy-utility trade-offs in distributed learning
Auditable and interpretable federated learning frameworks
Mobile Computing & Wireless Networks
Federated learning protocols for mobile, vehicular, and edge networks
FL in 6G networks and next-generation wireless systems
Multi-agent and swarm intelligence-based federated learning
Energy-aware and communication-efficient federated intelligence
Dynamic network topologies and adaptive FL protocols
Distributed inference and online learning for mobile networks
Cross-layer optimization for federated learning in wireless systems
Quality of service and latency-aware federated learning
Digital Twins & Cyber-Physical Systems
Federated intelligence for digital twin ecosystems
Digital twin generation and maintenance in distributed networks
Real-time federated learning for cyber-physical system monitoring
Distributed digital twins for smart cities and industrial IoT
Federated anomaly detection and predictive maintenance
Live model updating and synchronization in digital twin networks
Edge intelligence for decentralized digital twin ecosystems
Federated optimization for cyber-physical system control
Applications and Real-World Deployments
Smart cities and urban computing applications
Autonomous vehicles and intelligent transportation systems
Industrial IoT and manufacturing intelligence
Healthcare and medical federated learning systems
Financial services and fraud detection
Swarm robotics and distributed autonomous systems
Environmental monitoring and sustainability applications
Real-world case studies and deployment experiences
Economic models and incentive mechanisms for data federations
Regulatory compliance and legal frameworks (GDPR, EU AI Act, etc.)
Emerging Paradigms & Future Directions
Continual and lifelong learning in federated settings
Few-shot and zero-shot federated learning
Federated meta-learning and transfer learning
Neural architecture search in federated environments
Generative AI and federated learning convergence
Quantum-enhanced federated learning
Federated foundation models and large-scale pre-training
Neuromorphic computing and federated learning
Blockchain and distributed ledger technologies for FL
Sustainable and green federated learning approaches

Submission Types
Research Papers (up to 8 pages): novel methods/systems with rigorous evaluation.


Short Papers (up to 6 pages): promising early results, negative results with analysis, replication.


Format: Paper format
All papers should be in PDF format. Please make use of the appropriate IEEE template for conference proceedings to prepare your revised manuscript. Failure to do so may result in excluding your paper from the conference proceedings.
IEEE Word template can be found here (IEEE Conference Word Template).
IEEE Latex template can be found here (IEEE Conference Latex Template).
IEEE Overleaf Latex template can be found here (IEEE Overleaf Conference Latex Template).

Important Dates
Paper submission: October 1, 2025 (11:59 PM)
Notification of acceptance: October 10, 2025
Camera-ready deadline: October 22, 2025
All deadlines are in Anywhere on Earth (AoE) time.

Submission Portal
Papers should be submitted through Easychair at: [Submission portal link]
For submission guidelines, please visit: https://intelligent-systems.net/flics/

Contact Information
For questions about submissions, please contact: intelligent.systems2026@gmail.com
We look forward to receiving your contributions and to seeing you at FLICS 2025!