FLICS 2025

Call For Papers

Conference Scope

The Federated Learning and Intelligent Computing Systems (FLICS) Conference 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.

FLICS 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 2026 provides a unique platform for interdisciplinary collaboration, bridging theoretical foundations and practical implementations. The Conference welcomes contributions from both researchers and practitioners in the field of FL.

Journal Publication Opportunity

Selected papers with potential for further development may be invited to submit an extended version to a special track in one of the following journals:

Invitation-based
Expert Systems
Wiley
Invitation-based
Cluster Computing
Springer

Key Focus Areas

Federated Learning Systems & Edge Intelligence
  • 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

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

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

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

AI & Intelligent Systems for Smart Cities
  • AI-driven urban mobility: traffic flow optimization, multimodal transport, autonomous vehicles
  • Smart energy: predictive demand response, grid optimization, distributed energy resources
  • Urban sensing & IoT: federated and privacy-preserving analytics for large-scale data
  • Home and building automation: comfort, safety, and energy efficiency through edge AI
  • AI for public safety, emergency response, and disaster resilience
  • Urban digital twins: modeling, simulation, and real-time decision-making
  • Data governance, ethics, and fairness in city-scale AI deployments
  • Cross-domain integration: combining mobility, energy, health, and environment data for holistic intelligence
  • Real-world case studies and lessons learned from smart city pilots

Communication & Resource Efficiency
  • Model Compression & Quantization
  • Gradient Compression Techniques
  • Sparse Communication Protocols
  • Energy-efficient FL
  • Bandwidth-constrained Learning
  • Adaptive Communication Strategies
  • Hierarchical Federated Learning

Personalization & Fairness
  • Personalized Federated Learning
  • Meta-learning for FL
  • Fairness-aware FL
  • Bias Mitigation Techniques
  • Multi-objective FL
  • Clustered Federated Learning
  • Demographic Parity in FL

Edge Computing & IoT
  • Edge-Cloud Federated Learning
  • IoT Device Orchestration
  • Mobile Edge Computing
  • Fog Computing Integration
  • 5G/6G Network Optimization
  • Real-time FL Systems
  • Resource-constrained Devices

Advanced AI & ML Paradigms
  • Federated Reinforcement Learning
  • Federated Transfer Learning
  • Federated Deep Learning
  • Federated Graph Neural Networks
  • Federated Generative Models
  • Large Language Models in FL
  • Neuro-symbolic FL

Applications & Use Cases
  • Healthcare & Medical AI
  • Financial Services & FinTech
  • Autonomous Vehicles
  • Smart Cities & Infrastructure
  • Industrial IoT & Manufacturing
  • Natural Language Processing
  • Computer Vision Applications

Systems & Infrastructure
  • FL Frameworks & Platforms
  • Distributed System Design
  • Hardware Acceleration
  • Blockchain-based FL
  • Benchmarking & Evaluation
  • Simulation Environments
  • Performance Optimization

Emerging & Interdisciplinary
  • Quantum Federated Learning
  • Federated Continual Learning
  • Cross-modal Federated Learning
  • Federated Causal Inference
  • Sustainable & Green FL
  • Human-in-the-loop FL
  • Federated Explainable AI