ACM ECCAI Workshop 2025

Call for Papers

In recent years, with the rapid development of AI, AI workloads are increasingly being generated and executed at the edge. However, due to the limited computational capabilities of edge devices, edge-cloud collaboration has gained growing attention from researchers. At the same time, new research directions have emerged around edge-cloud networking tailored for AI workloads and dynamically adjusting network bandwidth to support such collaboration. Emerging AI technologies, such as edge-optimized deep learning and LLM-based network agents, further open up new possibilities for enhancing this paradigm.

ECCAI 2025 invites original research that explores these questions, aiming to bridge researchers and practitioners working at the intersection of AI, mobile systems, edge computing, edge-cloud networking, and edge-cloud collaboration. We seek high-quality submissions describing novel ideas, system designs, experimental studies, and real-world deployments in topics including, but not limited to:

1. Supporting the Deployment and Execution of AI Applications in Edge-Cloud Collaboration

a. Processing video stream data within edge-cloud collaborative systems
b. Optimizing support for real-time data processing at the edge, especially when data is generated at the edge and complex AI operations are performed in the cloud
c. Integrating and optimizing AI data with various special patterns across edge and cloud environments
d. Processing, acceleration, and accuracy enhancement of image, speech, and text data across edge and cloud

2. Leveraging AI technologies to enhance edge-cloud collaboration

a. Predicting workloads to determine whether tasks should be executed at the edge or in the cloud
b. Using reinforcement learning to adjust network bandwidth usage and compute resource allocation
c. Automatically compressing models to fit the constraints of edge devices
d. Employing large language models (LLMs) or agent systems to optimize edge-cloud collaboration

3. Applications and specific challenges of AI in edge-cloud collaboration

a. Privacy protection issues in edge-cloud collaboration
b. Applications of federated learning and knowledge distillation across edge and cloud environments
c. The use of multimodal learning in edge-cloud collaborative systems

4. Deployment Experiences and Case Studies

a. Large-scale deployments of AI workload on the edge devices and cloud
b. Cloud-edge-device collaboration for real-time, AI-enhanced services
c. Lessons learned from commercial or public deployments
d. Prototype systems demonstrating access-network-based inference
e. Edge-cloud collaboration for AI workloads in autonomous driving, smart manufacturing, and healthcare

Submission Instructions

Submissions must be original, unpublished work, and not under consideration at another conference or journal. Submitted papers must be at most six (6) pages long, excluding references and appendices, in two-column 10pt ACM format . Authors of accepted submissions are expected to present and discuss their work at the workshop. All submissions will be peer-reviewed, and the review process will be double-blind. Per the anonymity guidelines, please prepare your paper in a way that preserves the anonymity of the authors. No information will be shared with third parties.
Please submit your paper via https://eccai2025.hotcrp.com/