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ANNEI 2024 : International workshop on Artificial Neural Networks for Edge Intelligence

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Link: https://emergingtechnet.org/FMEC2024/Workshops/ANNEI2024/index.html
 
When Sep 2, 2024 - Sep 5, 2024
Where Malmö, Sweden
Submission Deadline Jun 30, 2024
Notification Due Jul 30, 2024
Final Version Due Aug 10, 2024
Categories    edge intelligence   mobile edge computing   cloud computing   artificial neural networks
 

Call For Papers

Call for Papers

Welcome to the International workshop on Artificial Neural Networks for Edge Intelligence (ANNEI 2024)! The workshop will discuss artificial intelligence (AI) optimization and its implementation in edge computing and Internet of Things (IoT) environments, with a focus on federated learning, artificial neural networks, and sparse neural networks. Federated Learning investigates a cooperative method that protects data privacy while allowing AI models to be trained over dispersed edge devices. Sparse ANNs are a viable option that can be deployed in edge computing/IoT scenarios since they address scalability issues and reduces energy usage. Sparsity approaches also allow the topology of ANNs to be smaller and more scalable, which makes them appropriate for deployment in resource-constrained situations like edge computing and IoT devices. Multidisciplinary research that combines edge computing with other emerging technologies, such as blockchain, artificial intelligence, cybersecurity technologies, etc., is highly welcomed. The potential topics include, but are not limited to:

Federated Learning: Privacy-Preserving Collaborative AI Training on Edge Devices.
Sparse Neural Networks: Principles, Advantages, and Applications in Edge Computing and IoT.
Optimization Techniques for AI Models in Resource-Constrained Environments.
Scalability Challenges and Solutions in Edge Computing and IoT Deployments.
Energy-Efficient Computing Strategies for Edge Devices and IoT Systems.
Exploring the Interplay between Federated Learning and Edge Computing Technologies.
Security and Privacy Considerations in Federated Learning and Edge AI Systems.
Real-world Case Studies of Federated Learning and Sparse Neural Networks in Edge Computing.
Integration of Edge Computing with Blockchain Technology for Secure and De-centralized AI Applications.
Advances in Cyber-security Technologies for Securing Edge Computing/IoT Environments.
Hardware Acceleration and Edge Computing Architectures for Efficient AI Inference.
Federated Learning for Healthcare Applications: Challenges and Opportunities.
Exploring Edge Computing and AI Integration in Smart Cities and Urban Infrastructure.
Future Directions and Emerging Trends in Edge Computing and IoT Integration with AI Technologies.

Organization Committee

Dr. Lucia Cavallaro, Radboud University, The Netherlands
Dr. Muhammad Azfar Yaqub, Free University of Bozen-Bolzano, Italy
Dr. Antonio Liotta, Free University of Bozen-Bolzano, Italy

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