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. 

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