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

2026-05-08

AI Summary

This study addresses unsupervised anomaly detection for on-device predictive maintenance in federated learning by systematically evaluating the performance, communication overhead, and personalization trade-offs of variational autoencoders (VAEs), generative adversarial networks (GANs), and denoising diffusion probabilistic models (DDPMs) under both full-federation and partial-federation settings—such as sharing only the decoder. The work proposes the first component-level sharing taxonomy for federated generative models, formalizing partial parameter sharing as a mechanism for model personalization. Experimental results demonstrate that, under non-IID data distributions and bandwidth-constrained conditions, DDPMs with shared decoders can outperform fully federated training. Moreover, VAEs and DDPMs exhibit consistently greater stability and robustness compared to GANs; although full-federation improves GAN stability, its overall performance remains inferior.

Abstract

Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT) environments. While discriminative models dominate most FL use cases, recent advances in generative models -- such as Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Diffusion Models (DM) -- offer new opportunities for unsupervised anomaly detection in time series analysis, with relevant applications in predictive maintenance (PdM) in critical industrial infrastructures. In this work, we present a comprehensive analysis of VAEs, GANs, and DMs in the context of federated PdM. We analyze their performance and communication overhead under both full and partial federation setups, where only subsets of model components are shared. Building on this analysis, the paper proposes a novel taxonomy for federated generative models that formalizes partial component sharing as a principled mechanism for model personalization. Our experiments over a real-world time series dataset reveal distinct trade-offs in model utility, stability, and scalability, especially in heterogeneous and bandwidth-constrained FL settings. For the evaluated GAN-based configurations, full federation improves training stability relative to independent local training, although the model remains less robust than the VAE- and DDPM-based alternatives. For DMs, however, partial federation -- especially decoder sharing -- can outperform full federation in bandwidth-constrained, non-IID settings.

Problem

• Federated Learning
• Generative Models
• Predictive Maintenance
• On-Device Learning
• Time Series Anomaly Detection

Innovation

• Federated Generative Models
• Partial Component Sharing
• Predictive Maintenance
• Diffusion Models
• Model Personalization