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