会議情報

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

会議のウェブサイトを表示するにはログインしてください
無料登録で公式サイトの閲覧、締切のトラッキング、メールリマインダーが利用できます。
新規登録 ログイン
締切カウントダウンバッジを埋め込む
FLTA
このデータを API で取得
検索とランキング一覧は資格情報なしで利用できます。このページの詳細データには無料の API キーが必要です。詳しくは開発者向けガイドをご覧ください。
投稿締切日:
2026-07-01
通知日:
2026-09-10
開催日:
2026-10-27
開催地:
Paris, France
開催回数:
閲覧: 11796   フォロー: 0   参加: 0

会伴インデックス (CP-I)

46.1 / 100
全 5,687 件中 第 2,541 位 · 上位 45%

人工知能・機械学習 分野 741 件中 第 275 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
43
コミュニティの注目度 (10%)
16
公開情報の充実度 (15%)
55

使用した入力: 確認できる開催回数：4 · 過去 24 か月にこのページを開いた研究者：8 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-25

論文募集

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
最終更新：Dou Sun（）

関連会議

関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971

コメント 0

まだコメントはありません。

コメントするにはログインしてください