会議情報

FLICS 2027: Symposium on Federated Learning and Intelligent Computing Systems

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
FLICS
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:
2027-02-20 残り 154 日
通知日:
2027-04-15
開催日:
2027-06-14
開催地:
Napoli, Italy
開催回数:
閲覧: 4790   フォロー: 0   参加: 0

会伴インデックス (CP-I)

44.4 / 100
全 5,682 件中 第 3,034 位 · 上位 54%

人工知能・機械学習 分野 739 件中 第 344 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%)
37
コミュニティの注目度 (10%)
12
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：3 · 過去 24 か月にこのページを開いた研究者：4 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 2026-09-19

論文募集

FLICS 2027 (Symposium on Federated Learning and Intelligent Computing Systems) is an academic conference held in Napoli, Italy on 2027-06-14. The paper submission deadline is 2027-02-20. Acceptance notifications are sent on 2027-04-15.

Conference scope The Federated Learning and Intelligent Computing Systems (FLICS) Conference brings together researchers, practitioners, and industry leaders to explore the convergence of federated learning with intelligent computing systems, edge AI, and autonomous workflows. As we advance toward 6G networks, pervasive edge intelligence, and decentralized cyber-physical systems, the need for collaborative, privacy-preserving learning approaches has never been more critical. FLICS 2027 features two main tracks that address complementary aspects of modern intelligent computing: Main Track 1 – Federated Learning Systems & Applications This track focuses on the fundamental challenges and innovations in federated learning, including scalable architectures, privacy and security mechanisms, communication efficiency, personalization, edge computing integration, and real-world deployments. This track addresses the core technical foundations of federated learning systems and their applications across diverse domains such as healthcare, finance, industrial IoT, and smart cities. Main Track 2 – Intelligent Computing Systems This track explores cutting-edge technologies and applications in intelligent computing systems, including large language models, generative AI, deep learning architectures, agentic AI workflows, digital twins, and smart city applications. This track bridges the gap between federated learning and emerging AI paradigms, addressing the systems, infrastructure, and interdisciplinary applications that drive the next generation of intelligent computing. FLICS 2027 provides a unique platform for interdisciplinary collaboration, bridging theoretical foundations and practical implementations. The Conference welcomes contributions from both researchers and practitioners across both tracks, fostering dialogue between federated learning specialists and intelligent computing systems experts. Key focus areas The conference features two main tracks. See details below about each track. Main Track 1 – Federated Learning Systems & Applications Federated Learning Systems & Edge Intelligence Scalable FL architectures and large-scale deployments Cross-silo and cross-device federated learning Hardware-aware and resource-efficient FL Communication-efficient FL (quantization, sparsification, compression) FL under client mobility and heterogeneity Benchmarks and evaluation frameworks for FL FL deployment in UAVs, mobile edge clouds, autonomous systems Communication & Resource Efficiency for FL Model and gradient compression Sparse and adaptive communication Energy-efficient FL Hierarchical and clustered FL Multi-objective optimization Privacy, Security, and Trust in FL Privacy-enhancing technologies Secure aggregation protocols and cryptographic methods Explainable and trustworthy FL Resilient FL against adversarial attacks Privacy–utility trade-offs Auditable FL frameworks Personalization & Fairness in FL Personalized FL Fairness-aware FL Meta-learning for FL Bias mitigation Clustered and multi-task FL Edge Computing, IoT, and Mobile/Wireless FL Edge–cloud FL architectures IoT orchestration FL in 5G/6G and vehicular networks Real-time FL systems Advanced FL Paradigms Federated deep learning and GNNs Federated reinforcement learning Federated generative models Neuro-symbolic FL Applications & Real-World Deployments Healthcare and medical AI Financial services and risk modeling Industrial IoT and predictive maintenance Smart cities and infrastructure NLP and computer vision via FL Emerging & Interdisciplinary FL Directions Continual and lifelong learning Quantum FL Neuromorphic FL Blockchain for FL Sustainable and green FL Main Track 2 – Intelligent Computing Systems Large Language Models, Generative AI & NLP LLM architectures and training Prompting, fine-tuning, alignment Multi-modal generative AI NLP for intelligent assistants Evaluation and robustness Deep Learning & Advanced Intelligent Systems Novel deep learning architectures Transformers, GNNs, hybrid models Continual learning and transfer learning Deep reinforcement learning Agentic AI & Autonomous Workflows Agentic AI systems and workflow automation Multi-agent systems and collaborative intelligence User–agent interaction and personalization Digital Twins, Cyber-Physical & Intelligent Systems Digital twins for industry and cities Real-time monitoring and simulation Edge AI for CPS Intelligent Systems for Smart Cities & Urban Computing Urban mobility optimization Smart energy systems Urban sensing and IoT AI for emergency response Urban digital twins Systems, Infrastructure & Platforms Distributed systems for AI workloads Hardware acceleration Performance and energy optimization Applications & Interdisciplinary Case Studies Healthcare and life sciences FinTech and risk modeling Industry 4.0 and robotics Education and digital services Sustainability and environmental monitoring
最終更新：Admin Agent （）

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