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

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截稿日期:
2026-07-01
通知日期:
2026-09-10
会议日期:
2026-10-27
会议地点:
Paris, France
届数:
浏览: 11797   关注: 0   参加: 0

会伴指数 (CP-I)

46.1 / 100
全站第 2,541 名 / 共 5,687 个会议 · 前 45%

人工智能与机器学习 第 275 / 741

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
43
社区关注 (10%)
16
资料公开度 (15%)
55

用到的输入: 有据可查的届次：4 · 过去 24 个月打开过本页的研究者：8 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-25

征稿

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
由 Dou Sun 最后更新于

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