会议信息

MLN 2026: International Conference on Machine Learning for Networking

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截稿日期:
2026-10-15 已截稿
通知日期:
2026-10-30
会议日期:
2026-12-01
会议地点:
Paris, France
届数:
浏览: 16992   关注: 0   参加: 0
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会伴指数 (CP-I)

50.0 / 100
全站第 1,689 名 / 共 5,759 个会议 · 前 30%

人工智能与机器学习 第 172 / 751 网络与通信 第 240 / 874

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%)
62
社区关注 (10%)
18
资料公开度 (15%)
55

用到的输入: 有据可查的届次：9 · 过去 24 个月打开过本页的研究者：10 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 1.2 · 算于 2026-10-24

征稿

MLN 2026 (International Conference on Machine Learning for Networking) is an academic conference held in Paris, France on 2026-12-01. The paper submission deadline is 2026-10-15. Acceptance notifications are sent on 2026-10-30.

MLN 2026 is the 9th International Conference on Machine Learning for Networking. The conference brings together researchers, practitioners, engineers, and industry experts working at the intersection of artificial intelligence, machine learning, communication networks, and distributed systems. MLN 2026 aims to foster multidisciplinary exchanges on the design, operation, optimization, security, and evolution of intelligent networks, as well as on the networking infrastructures required to support emerging AI systems. The conference welcomes original research contributions, experimental studies, system and prototype descriptions, industry experiences, datasets, benchmarks, and survey papers. MLN 2026 will be held in Paris, France, from December 1st to December 3rd, 2026. Artificial intelligence and machine learning are transforming the way communication networks are designed, deployed, operated, secured, and optimized. At the same time, the rapid development of foundation models, generative AI, agentic systems, and large-scale distributed learning is creating new networking requirements in terms of performance, scalability, reliability, energy efficiency, privacy, and security. MLN 2026 provides an international forum for presenting and discussing advances at the intersection of machine learning and networking. The scope of the conference encompasses two complementary perspectives: AI and machine learning for networks: the use of intelligent methods to analyze, automate, optimize, secure, and manage communication and distributed systems. Networks and distributed systems for AI: the design of communication infrastructures, protocols, architectures, and resource- management mechanisms that support AI training, inference, agent collaboration, and intelligent services. MLN 2026 encourages contributions that combine methodological advances with networking applications, as well as interdisciplinary work spanning artificial intelligence, telecommunications, distributed computing, cybersecurity, operations research, control, and data science. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal, to https://easychair.org/conferences/?conf=mln2026. The accepted papers will be published as a post-proceedings in Springer's LNCS. Lecture Notes in Computer Science (LNCS) series is indexed by the ISI Conference Proceedings Citation Index - Science (CPCI-S), included in ISI Web of Science, EI Engineering Index (Compendex and Inspec databases), ACM Digital Library, dblp, Google Scholar, Scopus, etc. Topics of interest include and are not limited to: Machine Learning and Artificial Intelligence for Networks Deep, reinforcement, graph, federated, and distributed learning Traffic prediction, anomaly detection, routing, and resource allocation Machine learning for 5G, 6G, IoT, satellite, and wireless networks Intelligent SDN, NFV, cloud, edge, and service orchestration Energy-efficient and sustainable communications Agentic AI and Autonomous Network Management Agentic AI for network operations and optimization Autonomous, zero-touch, and intent-based networks Multi-agent coordination and collaboration AI agents for control, fault detection, and remediation Human-in-the-loop automation, safety, and governance Generative AI, Foundation Models, and Network Digital Twins Large language models and foundation models for networking Generative AI for network design and management RAG, knowledge graphs, and natural-language interfaces Network digital twins and synthetic data generation Multimodal and resource-efficient models for cloud and edge Networking and Distributed Systems for AI Networks for distributed AI training and inference Data-center, cloud, edge, and high-performance AI networking Efficient federated learning and collaborative inference Joint optimization of communication, computing, and energy Architectures and protocols for AI-agent ecosystems Security, Trustworthiness, Experimentation, and Applications Machine learning for network security and resilience Adversarial AI and security of autonomous agents Explainable, trustworthy, and privacy-preserving AI Datasets, benchmarks, testbeds, and real-world deployments Intelligent applications for smart and critical systems Position papers are also welcome and should be clearly marked as such.
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