The International Symposium on Federated Learning Technologies and Applications (FLTA) is a premier venue for timely publication of archival research on FL management, systems, services and applications. FLTA aims to provide attendees with a comprehensive understanding of FL communication, computing, and system requirements. Through keynote speeches, panel discussions, and presentations, attendees will have the opportunity to engage with leading experts and learn about the latest developments and future trends in the field.

FLTA focuses on fostering an understanding of FL, identifying technical challenges, and exploring potential solutions, including distributed optimization, privacy-preserving techniques, intelligent learning algorithms, personalized FL, communication efficiency approaches, open challenges, and recent trends and opportunities. We welcome submissions addressing the important challenges (see the non-exhaustive list of topics below) and presenting novel research or experimentation results with system or network-related case studies. Survey papers that offer a perspective on related work and identify key challenges for future research will be considered as well. We look forward to your submissions!

Specific topics of interest include, but are not limited, to the following:

    Large-scale FL applications in IoT environments
    Applications of FL
    Blockchain for FL
    Data Heterogeneity in FL
    Device heterogeneity in FL
    Fairness in FL
    Hardware for on-device FL
    Federated transfer learning
    Adversarial attacks on FL
    Optimization advances in FL
    Partial participation in FL
    Personalization in FL
    Privacy Concerns in FL
    Privacy-preserving methods for FL
    Resource-efficient FL
    Systems and infrastructure for FL
    Theoretical contributions to FL
    Vertical FL
    Federated IoT
    Security in FL
    Explainable FL and AutoFL
    FL clients model heterogeneity, aspects and solutions
    Recommendations systems based on FL
    Clustering FL techniques
    Federated Reinforcement LearningFederated Learning with Non-IID Data
    Horizontal, Vertical and Transfer Federated Learning: challenges and opportunities
    FL approaches using traditional ML
    FL secure fusion functions
    Communications efficiency in FL