International Workshop on Edge Intelligence in conjunction with ACM SEC 2024, Rome, Italy, [December 7, 2024]

Edge intelligence is a new upcoming field that addresses the decentralized deployment of AI models at the network edge. This can relate to the deployment of such models on devices and embedded systems or at edge computing facilities. This new paradigm reduces latency, energy consumption and bandwidth usage while it also enhances privacy and security by minimizing data transmission or sharing. However, model composition and resource constraints are key challenges with respect to edge intelligence. In today's research landscape, edge intelligence garners significant interest due to its potential to revolutionize various domains, including the Internet of Things (IoT), autonomous systems, healthcare, precision agriculture, ambient augmentation, and smart cities. Its ability to enable autonomous decision-making, support real- time analytics using constrained edge device resources, and alleviate the burden on centralized cloud infrastructures makes it a compelling area for exploration and innovation.


Topics of interests

We solicit novel research contributions to the broad area of edge intelligence, such as (but not limited to):
  • Cloud-edge collaborative training and inference
  • Foundation models at the edge
  • Federated learning via edge systems in relation to safety, security, and privacy
  • Trustworthy edge-assisted artificial intelligence
  • Low-latency next-generation AI-native wireless networking
  • Efficient (deep) learning on edge devices
  • Efficient (distributed) inference on end devices
  • AI-assisted augmentation/extended reality (XR) applications, such as holographic/immersive communications
  • Lightweight container orchestration for domain-specific AI
  • Intelligent/autonomous transportation systems/services
  • AI-assisted cyber-physical/distributed automation applications (e.g. factory, farming, transportation automation)
  • Experimental edge computing testbeds for machine/deep learning
  • AI for trustworthy systems
  • Blockchain-based collaborative edge computing for AI

Instructions for Authors

Only electronic submissions in PDF will be accepted. Submitted papers must be written in English and must be rendered without error using standard PDF viewing tools. Submitted papers must be no longer than 6 single-spaced 8.5'' x 11''; pages, including figures and tables, but excluding references, and using 10-point type on 12-point (single-spaced) leading, two-column format, Times Roman, or a similar font, within a text block 7.14'' wide x 9.22'' deep. IEEE Standard template for Latex and Word meet these specifications and can be found at: https://www.ieee.org/conferences/publishing/templates.html. Papers not meeting these criteria will be rejected without review, and no deadline extensions will be granted for reformatting. Pages should be numbered, and figures and tables should be legible in black and white, without requiring magnification.


At least one author for each accepted paper for the Edge Intelligence Workshop 2024 must register for the workshop.


Submission website: https://ei24.hotcrp.com/


At least one of the authors of each paper accepted must register for the workshop.


Important Dates

Submission Deadline:   October 4, 2024 (Firm)

Notification: October 25, 2024

Camera-ready paper deadline: November 1, 2024

Workshop date: December 7, 2024


Organization

Edge Intelligence Workshop 2024 will be a half-day workshop with two paper presentation sessions, one keynote, and potentially a poster / short-paper presentation session.



Workshop Organizers

TPC co-chairs

Jaya Prakash Champati, IMDEA Networks Institute, Madrid

Prasad Calyam, University of Missouri-Columbia

Xiaoli Liu, University of Helsinki


Organizing Chair

James Gross, KTH Stockholm


Technical Program Committee

Saptarshi Debroy, City University of New York

Qi Zhang, Aarhus University

Xiang Su, Norwegian University of Science and Technology

Paul Pop, DTU Copenhagen

Roberto Morabito, EURECOM

Jianli Pan, George Mason University

Dianlei Xu, University of Helsinki

Nan Li, Kings College of London

Hei Victor Cheng, Aarhus University

Wei Bao, University of Sydney