arXiv:2505.23939v2 Announce Type: replace Abstract: This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and cu
The rapid expansion of IoT devices and increasing privacy concerns are driving the need for on-device AI solutions that minimize data sharing.
This development enables machine learning at the edge for privacy-sensitive applications, unlocking new use cases in critical sectors like healthcare and industrial IoT.
Machine learning model design can now occur directly on IoT gateways without sending raw data to the cloud, fundamentally changing how edge AI is deployed and secured.
- · IoT gateway manufacturers
- · Healthcare IoT (HIoT) providers
- · Industrial IoT (IIoT) solutions
- · Privacy-focused tech companies
- · Cloud-centric ML platforms
- · Data transmission infrastructure providers (for raw IoT data)
More robust and secure edge AI deployments will become feasible across various industries.
Reduced reliance on cloud infrastructure for initial model training and iteration for edge devices.
The development of more sophisticated, self-optimizing edge AI ecosystems that are resilient to network outages and data breaches.
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Read at arXiv cs.LG