  Industry 4.0 has brought numerous advantages, such as increasing productivity
through automation. However, it also presents major cybersecurity issues such
as cyberattacks affecting industrial processes. Federated Learning (FL)
combined with time-series analysis is a promising cyberattack detection
mechanism proposed in the literature. However, the fact of having a single
point of failure and network bottleneck are critical challenges that need to be
tackled. Thus, this article explores the benefits of the Decentralized
Federated Learning (DFL) in terms of cyberattack detection and resource
consumption. The work presents TemporalFED, a software module for detecting
anomalies in industrial environments using FL paradigms and time series.
TemporalFED incorporates three components: Time Series Conversion, Feature
Engineering, and Time Series Stationary Conversion. To evaluate TemporalFED, it
was deployed on Fedstellar, a DFL framework. Then, a pool of experiments
measured the detection performance and resource consumption in a chemical gas
industrial environment with different time-series configurations, FL paradigms,
and topologies. The results showcase the superiority of the configuration
utilizing DFL and Semi-Decentralized Federated Learning (SDFL) paradigms, along
with a fully connected topology, which achieved the best performance in anomaly
detection. Regarding resource consumption, the configuration without feature
engineering employed less bandwidth, CPU, and RAM than other configurations.
