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