• IEEE.org
  • IEEE CS Standards
  • Career Center
  • About Us
  • Subscribe to Newsletter

0

IEEE
CS Logo
  • MEMBERSHIP
  • CONFERENCES
  • PUBLICATIONS
  • EDUCATION & CAREER
  • VOLUNTEER
  • ABOUT
  • Join Us
CS Logo

0

IEEE Computer Society Logo
Sign up for our newsletter
FacebookTwitterLinkedInInstagramYoutube
IEEE COMPUTER SOCIETY
About UsBoard of GovernorsNewslettersPress RoomIEEE Support CenterContact Us
COMPUTING RESOURCES
Career CenterCourses & CertificationsWebinarsPodcastsTech NewsMembership
BUSINESS SOLUTIONS
Corporate PartnershipsConference Sponsorships & ExhibitsAdvertisingRecruitingDigital Library Institutional Subscriptions
DIGITAL LIBRARY
MagazinesJournalsConference ProceedingsVideo LibraryLibrarian Resources
COMMUNITY RESOURCES
GovernanceConference OrganizersAuthorsChaptersCommunities
POLICIES
PrivacyAccessibility StatementIEEE Nondiscrimination PolicyIEEE Ethics ReportingXML Sitemap

Copyright 2025 IEEE - All rights reserved. A public charity, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.

  • Home
  • /Digital Library
  • /Journals
  • /Sc
  • Home
  • / ...
  • /Journals
  • /Sc

CLOSED Call for Papers: Special Issue on Edge AI-as-a-Service

It is essential to deliver learning-based artificial intelligence (AI) capabilities through API service endpoints at the edge under the as-a-service model to meet the growing demand for integrating big data, Internet of Things (IoT), and cloud computing capabilities. The edge AI services expedite making data-driven decisions (e.g., optimizing control, coordination, investment, medical services allocation, etc.). A sample research challenge addresses learning and/or inferencing efficiency of the edge AI service (or microservice) under development that meets the requirements for minimum level of quality of service (QoS), processing power, energy consumption, robustness, extensibility, and/or DevOps. Policy-based federated learning services at edge are typical edge AI use cases in which data processing and management constraints need to be considered in terms of data origin, ownership, heterogeneity, security, and privacy. 5G/6G edge nodes provide unprecedented high QoS wireless operating environments for edge AI services and related applications.

This special issue aims at promoting high-quality research on recent advances in edge AI-as-a-service and at inspiring related research efforts. Topics of interest include, but are not limited to, the following:

  • Deep-learning services at the edge
  • Learning-based service/microservice systems running atop edge devices/nodes
  • Learning-based services/microservices hosted by 5G/6G edge nodes
  • Policy-based learning services at the edge
  • Edge-centric distributed or collaborative learning services
  • Edge-centric federated learning services
  • Learning-based event stream processing and/or contextual data enrichment of edge services
  • Learning-based edge service management, optimization, and/or continuous improvement
  • Learning-based security, privacy, trust, and/or risk management of edge services

Important Dates

  • Manuscript Submissions Due: March 31, 2021
  • First Round Notification: June 30, 2021
  • Revised Submissions Due: August 31, 2021
  • Final Decision Notification: September 30, 2021
  • Final Manuscript Submissions Due: October 31, 2021

Guest Editors

  • Prof. Andrzej Goscinski, Deakin University and RMIT
  • Prof. Elisa Bertino, Purdue University
  • Prof. Shangguang Wang, BUPT

Paper Submission

Please select “SI on Edge AI-as-a-Service” when submitting your manuscripts in the online system at https://mc.manuscriptcentral.com/tsc-cs. Please read the author guidelines before submitting. Every manuscript should be no more than 14 pages. Submitted manuscripts should not have been previously published nor be currently under review for publication elsewhere. Moreover, they should provide a minimum of 30% original technical contributions in comparison to previous publications.

LATEST NEWS
From Isolation to Innovation: Establishing a Computer Training Center to Empower Hinterland Communities
From Isolation to Innovation: Establishing a Computer Training Center to Empower Hinterland Communities
IEEE Uganda Section: Tackling Climate Change and Food Security Through AI and IoT
IEEE Uganda Section: Tackling Climate Change and Food Security Through AI and IoT
Blockchain Service Capability Evaluation (IEEE Std 3230.03-2025)
Blockchain Service Capability Evaluation (IEEE Std 3230.03-2025)
Autonomous Observability: AI Agents That Debug AI
Autonomous Observability: AI Agents That Debug AI
Disaggregating LLM Infrastructure: Solving the Hidden Bottleneck in AI Inference
Disaggregating LLM Infrastructure: Solving the Hidden Bottleneck in AI Inference
Read Next

From Isolation to Innovation: Establishing a Computer Training Center to Empower Hinterland Communities

IEEE Uganda Section: Tackling Climate Change and Food Security Through AI and IoT

Blockchain Service Capability Evaluation (IEEE Std 3230.03-2025)

Autonomous Observability: AI Agents That Debug AI

Disaggregating LLM Infrastructure: Solving the Hidden Bottleneck in AI Inference

Copilot Ergonomics: UI Patterns that Reduce Cognitive Load

The Myth of AI Neutrality in Search Algorithms

Gen AI and LLMs: Rebuilding Trust in a Synthetic Information Age

Get the latest news and technology trends for computing professionals with ComputingEdge
Sign up for our newsletter