Conference Information

FLTA 2026: IEEE International Conference on Federated Learning Technologies and Applications

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Submission Date:
2026-07-01
Notification Date:
2026-09-10
Conference Date:
2026-10-27
Location:
Paris, France
Years:
Viewed: 11793   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

46.1 / 100
Ranked #2,541 of 5,687 conferences · Top 45%

#275 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
43
Community attention (10%)
16
Public record completeness (15%)
55

Inputs used: Editions on record: 4 · Researchers who opened this page in the past 24 months: 8

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-25

Call For Papers

FLTA 2026 (IEEE International Conference on Federated Learning Technologies and Applications) is an academic conference held in Paris, France on 2026-10-27. The paper submission deadline is 2026-07-01. Acceptance notifications are sent on 2026-09-10.

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
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