Información de la conferencia

FLICS 2027: Symposium on Federated Learning and Intelligent Computing Systems

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Día de Entrega:
2027-02-20 Faltan 154 días
Fecha de Notificación:
2027-04-15
Fecha de conferencia:
2027-06-14
Ubicación:
Napoli, Italy
Ediciones:
Vistas: 4786   Seguidores: 0   Asistentes: 0

Índice Conference Partner (CP-I)

44,4 / 100
Puesto n.º 3.034 de 5.682 congresos · 54% superior

N.º 344 de 739 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%) Sin datos: se puntúa con la línea base neutra de 50 —
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
37
Atención de la comunidad (10%)
12
Integridad del registro público (15%)
55

Datos utilizados: Ediciones documentadas: 3 · Investigadores que abrieron esta página en los últimos 24 meses: 4

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Premios al mejor artículo (+2,3)
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Solicitud de Artículos

FLICS 2027 (Symposium on Federated Learning and Intelligent Computing Systems) is an academic conference held in Napoli, Italy on 2027-06-14. The paper submission deadline is 2027-02-20. Acceptance notifications are sent on 2027-04-15.

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 2027 features two main tracks that address complementary aspects of modern intelligent computing: Main Track 1 – Federated Learning Systems & Applications This track focuses on the fundamental challenges and innovations in federated learning, including scalable architectures, privacy and security mechanisms, communication efficiency, personalization, edge computing integration, and real-world deployments. This track addresses the core technical foundations of federated learning systems and their applications across diverse domains such as healthcare, finance, industrial IoT, and smart cities. Main Track 2 – Intelligent Computing Systems This track explores cutting-edge technologies and applications in intelligent computing systems, including large language models, generative AI, deep learning architectures, agentic AI workflows, digital twins, and smart city applications. This track bridges the gap between federated learning and emerging AI paradigms, addressing the systems, infrastructure, and interdisciplinary applications that drive the next generation of intelligent computing. FLICS 2027 provides a unique platform for interdisciplinary collaboration, bridging theoretical foundations and practical implementations. The Conference welcomes contributions from both researchers and practitioners across both tracks, fostering dialogue between federated learning specialists and intelligent computing systems experts. Key focus areas The conference features two main tracks. See details below about each track. Main Track 1 – Federated Learning Systems & Applications Federated Learning Systems & Edge Intelligence Scalable FL architectures and large-scale deployments Cross-silo and cross-device federated learning Hardware-aware and resource-efficient FL Communication-efficient FL (quantization, sparsification, compression) FL under client mobility and heterogeneity Benchmarks and evaluation frameworks for FL FL deployment in UAVs, mobile edge clouds, autonomous systems Communication & Resource Efficiency for FL Model and gradient compression Sparse and adaptive communication Energy-efficient FL Hierarchical and clustered FL Multi-objective optimization Privacy, Security, and Trust in FL Privacy-enhancing technologies Secure aggregation protocols and cryptographic methods Explainable and trustworthy FL Resilient FL against adversarial attacks Privacy–utility trade-offs Auditable FL frameworks Personalization & Fairness in FL Personalized FL Fairness-aware FL Meta-learning for FL Bias mitigation Clustered and multi-task FL Edge Computing, IoT, and Mobile/Wireless FL Edge–cloud FL architectures IoT orchestration FL in 5G/6G and vehicular networks Real-time FL systems Advanced FL Paradigms Federated deep learning and GNNs Federated reinforcement learning Federated generative models Neuro-symbolic FL Applications & Real-World Deployments Healthcare and medical AI Financial services and risk modeling Industrial IoT and predictive maintenance Smart cities and infrastructure NLP and computer vision via FL Emerging & Interdisciplinary FL Directions Continual and lifelong learning Quantum FL Neuromorphic FL Blockchain for FL Sustainable and green FL Main Track 2 – Intelligent Computing Systems Large Language Models, Generative AI & NLP LLM architectures and training Prompting, fine-tuning, alignment Multi-modal generative AI NLP for intelligent assistants Evaluation and robustness Deep Learning & Advanced Intelligent Systems Novel deep learning architectures Transformers, GNNs, hybrid models Continual learning and transfer learning Deep reinforcement learning Agentic AI & Autonomous Workflows Agentic AI systems and workflow automation Multi-agent systems and collaborative intelligence User–agent interaction and personalization Digital Twins, Cyber-Physical & Intelligent Systems Digital twins for industry and cities Real-time monitoring and simulation Edge AI for CPS Intelligent Systems for Smart Cities & Urban Computing Urban mobility optimization Smart energy systems Urban sensing and IoT AI for emergency response Urban digital twins Systems, Infrastructure & Platforms Distributed systems for AI workloads Hardware acceleration Performance and energy optimization Applications & Interdisciplinary Case Studies Healthcare and life sciences FinTech and risk modeling Industry 4.0 and robotics Education and digital services Sustainability and environmental monitoring
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