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NetAISys 2025 : The 3nd International Workshop on Networked AI Systems (NetAISys), colocated with MobiSys 2025 | |||||||||||||||
Link: https://netaisys.github.io/ | |||||||||||||||
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Call For Papers | |||||||||||||||
Dear Researchers / Authors, You are invited to submit your high-quality research findings to the 3rd Workshop on Networked AI Systems (NetAISys) 2025, AI at the Edge & Beyond for Emerging Computing Frontiers. June 27, 2025 - Anaheim, California, US NetAISys co-located with ACM MobiSys 2025 Website: https://netaisys.github.io Submission link: https://netaisys25.hotcrp.com/ --------------- Aims and Scope --------------- The integration of AI and networking technologies is driving the development of intelligent and autonomous systems capable of making decisions and performing tasks in real-time. These systems are critical for a wide range of applications, including 5G and beyond networks, IoT, and edge computing. Furthermore, as we transition toward 6G networks, the potential of foundation models and federated learning to revolutionize networked AI systems is becoming increasingly evident. These advancements promise to enhance decision-making capabilities, optimize resource utilization, and enable more seamless collaboration across devices and infrastructures. However, designing, implementing, and deploying AI systems in networked environments presents a number of unique challenges. One of the main challenges is ensuring that AI systems can operate effectively in dynamic network environments with varying network conditions. Another requirement is balancing the trade-offs between the computational and communication requirements of AI systems in networked environments. Additionally, there is a growing emphasis on energy-efficient AI deployment to address sustainability concerns, alongside the need for new algorithms and protocols that can effectively utilize network resources while ensuring robustness and security. Finally, networked AI systems must handle the high-dimensionality and heterogeneity of the data they generate, requiring novel data management and analytics techniques tailored to real-world constraints. The goal of this workshop is to bring together researchers and practitioners working on the design, implementation, and deployment of AI systems in networked environments. The workshop aims to provide a forum for discussing the latest research and development in this field, as well as for sharing practical experiences and case studies. By bringing together experts from academia and industry, the workshop aims to foster collaboration and to promote the development of new ideas and research directions in this field. The workshop is co-located with ACM MobiSys 2025 and will be held as an in-person event in Anaheim (California, US). We invite submissions of original research papers, as well as papers describing practical experiences, case studies, and tutorials. Topics of interest include, but are not limited to: Distributed and collaborative AI algorithms and systems IoT, Cyber-Physical Systems (CPS), edge and fog computing for networked AI systems AI-enabled real time networking architectures and protocols for networked AI systems Edge AI-centric Networked AI Systems Enhanced intelligent networking techniques (e.g., slicing, placement, management, control, reconfiguration, virtualisation) for 5G and beyond Theoretical and/or experimental results addressing the predictability of networked AI systems Security and privacy for networked AI systems Data management, sharing, and sets for AI in networked AI systems Federated and collaborative learning for networked AI systems ---------------- Important dates ---------------- - Paper Submission: April 19, 2025, AoE (FIRM) - Acceptance Notification: April 25, 2025, AoE - Camera Ready: May 1, 2025. AoE Thank you for your consideration. Organizers: Roberto Morabito, EURECOM (FR) SiYoung Jang, Nokia Bell Labs Cambridge (UK) Chee Wei Tan, Nanyang Technological University (SG) Suman Banerjee, University of Wisconsin-Madison (US) Bryan Donyanavard, San Diego State University (US) Woojoong Kim, Pure Storage (US) Tingjun Chen, Duke University (US) |
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