9th International Conference on Machine Learning for Networking (MLN'2026)
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Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal, to https://easychair.org/conferences/?conf=mln2026.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 (pending). 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:
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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 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
- 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
- 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
- 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
Agentic AI and Autonomous Network Management
Generative AI, Foundation Models, and Network Digital Twins
Networking and Distributed Systems for AI
Security, Trustworthiness, Experimentation, and Applications
Position papers are also welcome and should be clearly marked as such.