Conference Information

ICMLCN 2025: IEEE International Conference on Machine Learning for Communication and Networking

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Submission Date:
2024-10-31
Notification Date:
2025-01-31
Conference Date:
2025-05-26
Location:
Barcelona, Spain
Years:
2
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Call For Papers

ICMLCN 2025 (IEEE International Conference on Machine Learning for Communication and Networking) is an academic conference held in Barcelona, Spain on 2025-05-26. The paper submission deadline is 2024-10-31. Acceptance notifications are sent on 2025-01-31.

The second IEEE International Conference on Machine Learning in Communications and Networking will be held in Barcelona, Spain. The conference brings together researchers from the disciplines of Machine Learning (ML), Communication and Networking and Signal Processing, and aims at promoting fundamental and applied research of ML for designing and analyzing communication systems and networks, for developing communication protocols to support ML services, as well as for advancing distributed ML over communication networks. The conference targets any communication and networking area, such as, but not limited to, wireless, satellite, optical, or molecular communications, Internet, and WLAN. TOPICS • ML and molecular networks • ML and optical networks • ML in Intelligent reflecting surfaces • ML and networking in smart cities • Decentralized transmission optimization for edge learning • Distributed multi-agent reinforcement learning • Edge learning in wireless networks • Experiments and testbeds • Integrated distributed optimization with edge learning • Integrated sensing and communication via ML • Network architectures and protocols for edge learning • Over-the-air computation for edge learning • Privacy and security issues with ML over networks • Trustworthiness in ML decisions • ML and networking for intelligent transportation systems • ML and networking for smart grids • ML for signal processing in communication and networking • ML for the MAC layer • ML for the physical layer • ML for the transport layer • Communication-efficient distributed ML • Distributed ML over communication networks • Federated learning and communications • Learn to transmit and receive • Resource management and network optimization • Semantic communications and ML • Wireless communication to support ML services • Wireless networking to support ML services • ML and IoT • Large language models (LLMs) for communication and networking
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