Call for papers - Discover Computing Journal (Springer Nature) - special collection "Augmenting Edge AI with Collaborative Edge Computing"
Edge AI enables AI processing on resource-limited devices at the network edge, supporting latency-sensitive, data-intensive applications. Its potential grows when trusted devices collaborate, improving performance, energy efficiency, and AI task quality. This collection seeks research on collaborative Edge AI applications, frameworks, offloading, secure middleware, benchmarking, and social or environmental impacts.
in Special issues by Cristian Mateos on August 14, 2025

** Call for Papers ** 

Discover Computing Journal (Springer Nature - https://link.springer.com/journal/10791)
Special collection on "Augmenting Edge AI with Collaborative Edge Computing: From Trusted Nodes to Ubiquitous Personal Devices"

** Aims and scope ** 

Edge AI enables intelligent data processing at the closest edge and hence the execution of smart, novel latency-sensitive and data-intensive applications in numerous domains. A key aspect of Edge AI is the ability to execute complex artificial intelligence algorithms on resource-limited devices. However, the potential of Edge AI lies not only in the capabilities of individual devices to run AI tasks, but also in the enhanced performance that can be achieved when groups of co-located, trusted devices operate collaboratively, which might further improve results quality and energy consumption for AI inference and training tasks, paving the way for Collaborative Edge AI. Collaboration typically involves dedicated and trusted edge devices such as single-board computers, UAVs, FPGAs, and edge nodes, while personal devices like wearables and smartphones are less frequently considered. Year to year, societies all over the world invest in renewing or acquiring personal devices with improved computing capabilities that remain underutilized for long periods. Together with traditional edge computing devices, these personal devices represent a massively deployed distributed infrastructure, with computing resources at the closest edge whose integration to collaborative Edge AI has been scarcely explored.

This collection solicits state-of-the-art research addressing a broad spectrum of challenges and opportunities, including novel collaborative edge AI applications, edge AI-specific programming frameworks and languages, improved edge AI offloading frameworks, and mechanisms/protocols to implement scalable, robust, fault-tolerant, secure, and private middleware services that include dedicated edge and/or consumer devices as computing resource providers in such settings. Literature surveys and benchmarking studies are also welcome. We also welcome in-depth discussions and empirical studies of the sociological aspects (e.g., ethical issues, incentive mechanisms, societal consequences) and implications (e.g. environmental benefits) of collaborative edge AI. 

** Submission **

You can find all the details about the collection here: https://link.springer.com/collections/heecbfgcci
Authors are welcome to submit their manuscripts using the following link and selecting the appropriate collection: https://submission.nature.com/new-submission/10791/3
The deadline for submissions is May 31, 2026.

** About the editors **

Asst. Prof. Matías Hirsch, PhD, ISISTAN (UNICEN-CONICET), Argentina.
Assoc. Prof. Cristian Mateos Diaz, PhD, ISISTAN (UNICEN-CONICET), Argentina. 
Prof. Tim A. Majchrzak, PhD, Ruhr University Bochum, and Center for Advanced Internet Studies (CAIS), Germany. 

** Contact **

You can reach us through matias.hirsch@isistan.unicen.edu.ar