Conference Information

FLDI 2027: International Conference on Federated Learning and Distributed Intelligence

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Submission Date:
2026-12-10 Due in 37 days
Notification Date:
2027-02-01
Conference Date:
2027-03-15
Location:
San Antonio, Texas, USA
Years:
2
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Call For Papers

FLDI 2027 (International Conference on Federated Learning and Distributed Intelligence) is an academic conference held in San Antonio, Texas, USA on 2027-03-15. The paper submission deadline is 2026-12-10. Acceptance notifications are sent on 2027-02-01.

Federated learning and distributed intelligence are rapidly emerging as key technologies for enabling collaborative artificial intelligence across decentralized and heterogeneous computing environments. By allowing multiple devices, organizations, and edge systems to jointly train and improve intelligent models without directly sharing their raw data, federated learning offers important advantages in privacy, scalability, data governance, and efficient use of distributed resources. These capabilities are becoming increasingly important for applications involving edge computing, Internet of Things (IoT), healthcare, smart cities, autonomous systems, cybersecurity, and next-generation communication networks. The 2nd International Conference on Federated Learning and Distributed Intelligence (FLDI 2027) provides an international forum for researchers, academics, industry professionals, and practitioners to present and discuss recent advances in federated learning, distributed artificial intelligence, collaborative machine learning, and intelligent decentralized systems. The conference aims to encourage the exchange of new ideas, methodologies, architectures, and practical solutions for building secure, efficient, scalable, and trustworthy distributed intelligence. FLDI 2027 welcomes original research and practical contributions covering areas such as federated and decentralized learning algorithms, privacy-preserving machine learning, distributed optimization, edge intelligence, federated large language models, communication-efficient learning, security and robustness, resource management, heterogeneous and personalized federated learning, and real-world applications. The conference seeks to promote interdisciplinary collaboration and advance the development of distributed intelligent systems capable of operating effectively across increasingly connected and data-driven environments. We invite the submission of original papers on all topics related to Intelligent Systems with special interest in but not limited to: Federated Learning Foundations and Algorithms Federated optimization and learning algorithms Personalized and heterogeneous federated learning Asynchronous and decentralized federated learning Communication-efficient federated learning Convergence, scalability, and performance analysis Distributed and Edge Intelligence Edge AI and distributed machine learning Collaborative and decentralized intelligence Intelligent edge-cloud systems Resource-aware distributed learning Multi-agent and swarm intelligence Privacy, Security, and Trustworthy Federated Learning Privacy-preserving machine learning Differential privacy and secure aggregation Attacks, defenses, and adversarial robustness Trust, reputation, and incentive mechanisms Blockchain-enabled federated learning Federated Learning for Generative AI and Large Models Federated large language models Federated foundation and multimodal models Distributed training and fine-tuning of large models Parameter-efficient federated learning Federated generative AI and AI agents Systems, Platforms, and Resource Management Federated learning architectures and frameworks Communication and network optimization Client selection and resource allocation Energy-efficient federated learning Deployment, orchestration, and benchmarking Applications of Federated and Distributed Intelligence Healthcare and biomedical applications IoT, smart cities, and intelligent transportation Cybersecurity and intrusion detection Autonomous systems and robotics 5G/6G networks, industrial systems, and smart environments
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