MLN 2024 is the seventh edition of the International Conference on Machine Learning for Networking. The goal of the conference is to provide a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-process on all aspects of Machine Learning and Networking. Each year, MLN attendees will appreciate and benefit from multidisciplinary exchanges on these hot topics. MLN 2024 will be held in Reims, France, from November 27th to November 29th, 2024.

Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal, to https://easychair.org/conferences/?conf=mln2024.

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:

    Deep and Reinforcement learning
    Pattern classification for networks
    Machine learning for network slicing optimization
    Machine learning for 5G/6G and beyond
    Machine learning for user behavior prediction
    Innovative machine learning methods
    Optimization of machine learning methods
    Performance analysis of machine learning algorithms
    Experimental evaluations of machine learning
    Data mining in heterogeneous networks
    Machine learning for Internet of Things
    Machine learning for security and privacy
    Distributed and decentralized machine learning algorithms
    Intelligent cloud-support communications
    Intelligent resource allocation
    Intelligent energy-aware/green communications
    Intelligent software defined networks
    Intelligent cooperative networks
    Intelligent positioning and navigation systems
    Intelligent wireless communications
    Intelligent underwater sensor networks
    Large Language Models for Networking

Position papers are also welcome and should be clearly marked as such.