On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems

The paper evaluates Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models for unsupervised anomaly detection in federated predictive maintenance. It compares full and partial federation setups, showing that full federation improves GAN performance but VAE and DDPM models are more robust. Decoding‑only sharing in Diffusion Models achieves similar utility to full federation while cutting communication costs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• diffusion
• benchmarks
• safety