Organizers
Shuai Wang
Track Chair
Assistant Professor, University of Electronic Science and Technology of China
Yanmeng Wang
Co-chair
Assistant Professor, Nanjing University of Posts and Telecommunications
Yanqing Xu
Co-chair
Research Assistant Professor, The Chinese University of Hong Kong (Shenzhen)
Xiuhua Wang
Co-chair
Associate Professor, Huazhong University of Science and Technology
Yiwei Li
Co-chair
Assistant Professor, Xiamen University of Technology
Abstract
Distributed intelligence is emerging as a central paradigm for 6G and future networked systems, fundamentally reshaping both wireless communications and machine learning. By integrating artificial intelligence with advanced networking infrastructures—including edge computing, IoT mesh networks, ultra-dense deployments, Integrated Sensing and Communication (ISAC), and programmable 5G/6G architectures—this paradigm enables large-scale collaborative inference across heterogeneous devices and multi-tier networks. This integration facilitates fast, responsive intelligence directly at the point of demand through real-time model sharing, cross-device coordination, and topology-aware learning, supporting applications ranging from autonomous systems, smart industries, AI-native network optimization, and the emerging urban low-altitude economy. However, practical implementation of distributed intelligence faces significant challenges in communication-computation co-design, cross-layer optimization, dynamic resource allocation, data/communication heterogeneity across network nodes, and security vulnerabilities including model/data poisoning, privacy leakage, and adversarial attacks. This track focuses on foundational theories, enabling technologies, and secure system designs at the intersection of distributed learning and intelligent networking, aiming to build scalable, adaptive, and privacy-preserving distributed intelligence for pervasive 6G connectivity.
Topics
The scope of this track encompasses distributed intelligence and networking technologies, with a main focus on enabling communication-efficient learning, resource-aware optimization, security preservation, and network intelligence under complex networking environments. We invite original, completed, and unpublished research that is not currently under review by any other journal, magazine, or conference. Topics of interest include, but are not limited to:
- Distributed machine learning over challenging wireless networks
- Communication-efficient distributed learning and advanced model compression
- Dynamic resource allocation and device scheduling for distributed intelligence
- Energy-aware distributed learning systems
- Federated learning and edge intelligence
- Security and privacy in distributed learning systems
- Byzantine-resilient distributed learning frameworks
- Multi-agent reinforcement learning for networked systems
- Integrated Sensing and Communication (ISAC) with distributed intelligence
- AI-native network control and optimization with distributed intelligence
- Semantic communication for distributed learning systems
- Digital twin-enabled distributed intelligence
- Distributed inference and collaborative perception
- Distributed intelligence with edge–server–cloud collaboration
- Distributed intelligence for urban low-altitude economy
- Distributed intelligence for industrial IoT systems
Invited Speakers (more will be announced)
The following invited speakers are planned:
- Assistant Professor, Yi Zhang, Xiamen University
- Assistant Professor, Yu Zhang, Anhui University