About the Workshop

With the advent of the Internet of Things (IoT), the number of devices at the edge increases significantly. The traditional cloud computing paradigm handles the huge volume of data produced by IoT devices by providing storage and processing facilities; however, it poses a significant end-to-end service latency, which is not acceptable for serving time-critical IoT applications such as healthcare, transportation, and manufacturing.

The development of edge/fog computing complements traditional cloud computing while serving time-critical IoT applications in a stipulated time. In such scenarios, Artificial Intelligence (AI) and Machine Learning (ML) can play a promising and important role in intelligent decision-making to enable these resource-constrained devices to serve IoT applications efficiently. Additionally, with the advancement of quantum computing, there is a high demand for quantum edge networks that integrate quantum communication and computing principles into edge computing architectures.

The A4E Workshop intends to leverage technological advancements and techniques in the applications of AI/ML for Fog and Edge networks. It provides a platform for the global communication and networking research community to share research findings relevant to addressing the challenges in applying AI techniques in heterogeneous, resource-constrained environments.

Workshop Paper Submission

Authors are invited to submit original, unpublished research papers that are not currently under review elsewhere. Submissions will undergo a rigorous peer-review process conducted by the Technical Program Committee based on:

• Originality
• Technical quality
• Novelty
• Significance
• Relevance to the workshop
• Clarity of presentation

Accepted papers will be included in the IEEE PerCom 2027 Workshop Proceedings and submitted for publication in the IEEE Xplore Digital Library, subject to IEEE publication policies.

Topics of Interest

The scope of this workshop includes, but is not limited to, the following technical vectors:

• AI/ML for Smart Healthcare IoT networks
• AI/ML for Smart Agricultural IoT networks
• AI/ML for Smart Transportation and fleet management
• AI/ML for Industrial IoT (IIoT) applications
• AI/ML for IoT-based Logistic Network Management
• Energy-efficient Federated Learning (FL) and DL for IoT
• FL/DL to support streaming applications at the Edge
• Intelligent service discovery and recommendation
• Dynamic configuration of edge/fog networks using AI/ML
• Light-weight FL/DL Algorithms for IoT Networks
• AI/ML-enabled offloading for IoT Networks
• Quantum AI/ML for edge-cloud computing
• Quantum computing for IoT networks
• Trust, privacy, and security issues for/of AI/ML in Edge/Fog
• Resource management for Edge/Fog through big data mining
• QoS modelling, measurement, and optimization of Edge services
• Energy optimization and cost minimization of Edge/Fog services
• Fundamental limits of edge learning systems

Workshop Organizers

Past Editions

The A4E workshop has a rich, proven track record of bringing the community together across flagship venues:

• A4E 2025: Co-located with IEEE GLOBECOM
• A4E 2023: Co-located with IEEE GLOBECOM
• A4E 2022: Co-located with IEEE INFOCOM