Call For Papers

The 7th edition of the KuVS Fachgespr?ch "Machine Learning in Networking (MaLeNe)" will be held on March 19–20, 2026 at the 新万博体育下载_万博体育app【投注官网】 of Augsburg in Augsburg, Germany.

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What is a Fachgespr?ch?

Fachgespr?che are a low-key version of workshops, organized (among others) by the Communication and Distributed Systems group of the Gesellschaft für Informatik. They foster community building by organizing meetings on topics of current interest, with a low entry barrier. The intended audience primarily comprises advanced Master’s students and early-stage PhD researchers, but of course everybody is welcome. Participants are invited to submit full papers, early-stage research ideas, extended abstracts, or work in progress.

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What is MaLeNe?

MaLeNe is one such Fachgespr?ch, dedicated to the interaction of machine learning and networking. Earlier editions can be found here: 2020a, 2020b, 2021 (1^st workshop), 2022, 2023 (2^nd workshop), 2025 (3^rd workshop).
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In recent years, communication networks have become highly flexible through the employment of virtualization and softwarization paradigms. Still, networks are highly complex, dynamic, and time-varying systems, such that the statistical properties of networks and network traffic cannot be easily understood and modeled. Furthermore, the interplay between networking and the dynamic and heterogeneous requirements, expectations, and experiences of applications and users increases the complexity of the systems, which makes fault, configuration, performance, and security management in networks a hard problem. As observed in other disciplines, the successful application of machine learning can help to overcome these issues by following a more data-driven approach. In the networking domain as well, technological advancements in the area of machine learning, the increasing availability of network analytics data, and the flexibility of programmable networks and virtualized network resources have made this approach applicable, which creates exciting new opportunities.
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MaLeNe 2026 aims to provide a forum for researchers addressing emerging concepts and challenges related to machine learning in networking. The Fachgespr?ch will address opportunities where machine learning can bring benefits to networking in different facets, such as network monitoring, management, and security. Together with flexible and programmable networks, this paves the way toward a more proactive and autonomous network design and “self-driving” networks. The long-term vision is that configuration decisions can be made in real time in an automated fashion before service and experience degradation occurs. The Fachgespr?ch will feature original paper presentations that foster discussions and joint work among participants.

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Topics of Interest

Authors are invited to submit extended abstracts, early-stage research ideas, or work in progress related to the topic areas listed below:

• Methodology
  • Data sets for benchmarking, verification, proof of concept
  • Data augmentation; dimensionality reduction (e.g., autoencoder); prediction and generation (e.g., GANs)
  • Performance evaluation methodology (best practices)
• Artificial Intelligence and Machine Learning Methods
  • Classical and deep learning methods for supervised, unsupervised, and reinforcement learning
  • Advanced methods (e.g., self-supervised learning) and models (e.g., adversarial, agentic, generative)
  • Large language models and foundation models; tiny models
• Generalizability
  • Transfer of trained models (e.g., small to large networks, enterprise to data center)
  • Federated learning (combine models trained for different data sets) and privacy
  • Context drift, catastrophic forgetting, and machine unlearning
• Explainability
  • Explainable Artificial Intelligence (XAI) and visualization
  • Understanding decisions of ML-based systems (management, traffic engineering, etc.)
  • Game-theory-based approaches to approximate guarantees
• Networking for Machine Learning and AI
  • Network architectures, applications, and use cases (data center, enterprise, etc.)
  • Network resource management (algorithms, schedulers, etc.)
  • In-network computation and processing
• Applications in Networking
  • Network monitoring, especially from encrypted traffic (e.g., traffic classification, QoE)
  • Network configuration (e.g., suggest optimal configurations, “spell-check” text-based configuration data)
  • Network planning (e.g., reconfigurable data centers, job placement)
  • Network management (e.g., autonomous management, self-driving networks)
  • Network security (e.g., intrusion detection, covert channels, firewall)
  • Advanced networks (e.g., 5G to 6G, industry, slicing)

Paper Submission

Authors may submit extended abstracts, early-stage research ideas, or works in progress presenting original research, ongoing studies, or practical experiences. Previously published work or work currently under review elsewhere cannot be submitted. Each submission must be written in English, accompanied by a 75 to 200 word abstract and a list of up to 5 key words. There is a length limitation of 2 A4 (210?mm × 297?mm) pages for full papers (including title, abstract, figures, tables) plus 1 page for references. Submissions must be in 2-column IEEE conference style with a minimum font size of 10?pt. Papers exceeding these limits, multiple submissions, and self-plagiarized papers will be rejected without further review. Authors should submit their papers electronically via the EasyChair online submission system under the following link: https://easychair.org/conferences?conf=malene2026

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Important Dates

• Paper submission deadline: January 15, 2026
• Paper notification: January 31, 2026
• Camera-ready deadline: February 16, 2026

Proceedings

All papers accepted for MaLeNe 2026 will be included in proceedings, which are published open access via OPUS. We reserve the right to remove any paper from the proceedings if the paper is not presented at the workshop.

Organizers

• Michael Seufert (新万博体育下载_万博体育app【投注官网】 of Augsburg)
• Andreas Blenk (Siemens AG)
• Bj?rn Richerzhagen (Siemens AG)