arXiv cs.AI· Usevalad Milasheuski, Piero Baraldi, Enrico Zio, Stefano Savazzi·· 13 小时前AI 评分18
联邦预测性维护系统中端侧生成模型的权衡分析
On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
AI 导读
研究系统分析了 VAE、GAN 与扩散模型在联邦预测性维护场景下的性能与通信开销，覆盖完全联邦与部分联邦两种设置，并在五个客户端、两个工业时间序列基准上验证。结果显示，GAN 配置下完全联邦相比独立本地训练能提升最终异常检测效果，但鲁棒性仍弱于 VAE 和 DDPM 方案；对扩散模型而言，仅共享解码器即可达到与完全联邦相当的效果，同时大幅降低通信负载。
正文 · 原文
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 split-parameter personalized FL, this paper provides an empirical instantiation of shared/private parameter partitioning for unsupervised generative Time-Series Anomaly Detection (TSAD), mapping the split to analysis and synthesis modules across the generative architectures. Our experiments over five clients and two industrial time-series benchmarks reveal distinct trade-offs in model utility, stability, and communication efficiency in heterogeneous cross-silo FL. For the evaluated GAN-based configurations, full federation improves final anomaly-detection utility relative to independent local training, although these models remain less robust than the VAE- and DDPM-based alternatives. For DMs, decoder sharing matches full federation while substantially reducing the communication payload.
| Comments: | Published in IEEE Internet of Things Journal, 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.07860 [cs.LG] |
| (or arXiv:2605.07860v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.07860 arXiv-issued DOI via DataCite |
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| Journal reference: | IEEE Internet of Things Journal, 2026 |
| Related DOI: | https://doi.org/10.1109/JIOT.2026.3739066
DOI(s) linking to related resources |
Submission history
From: Usevalad Milasheuski [view email]
[v1]
Fri, 8 May 2026 15:20:22 UTC (2,782 KB)
[v2]
Tue, 27 Oct 2026 10:33:35 UTC (2,325 KB)
来源：arXiv cs.AI · arxiv.org