In this context, Federated learning (FL) has emerged as a prospective solution that facilitates distributed collaborative learning without disclosing original training data. The idea behind FL is to train the ML model collaboratively among distributed actors without sharing their data and violating the privacy accord. FL locates ML services and operations closer to the clients, facilitating leveraging available resources on the network’s edge. Hence, FL has become a critical enabling technology for future intelligent applications in domains such as autonomous driving, smart manufacturing, and healthcare. This development will lead to an overall advancement of FL and its impact on the community, noting that FL has gained significant attention within the machine learning community in recent years.

The FLTA conference aims to provide a global forum for disseminating the latest scientific research and industry results in all aspects of federated learning. FLTA also aims to bring together researchers, practitioners, and edge intelligence advocators in sharing and presenting their perspectives on the effective management of FL deployment architectures. The conference will address the theoretical foundations of the field, as well as applications, datasets, benchmarking, software, hardware, and systems. Also, to create an annual forum for researchers and practitioners who share an interest in FL. FLTA offers an opportunity to showcase the latest advances in this area and discuss and identify future directions and challenges in FL systems. FLTA will also provide ample opportunities for networking, sharing knowledge, and collaborating with others in the metaverse community.

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
    Recommendation systems based on FL
    Clustering FL techniques
    Federated Reinforcement Learning
    Federated 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