Automated Predictive Maintenance Optimization via Federated Learning and Dynamic Bayesian Network Causal Inference in Semiconductor Fabrication

**Originality:** This research introduces a novel federated learning framework coupled with dynamic Bayesian network (DBN) causal inference to optimize predictive maintenance schedules in semiconductor fabrication. Unlike traditional centralized approaches, our methodology leverages decentralized data from various equipment nodes while preserving data privacy and allowing for accurate causal inference of failure modes, fundamentally reshaping maintenance strategies to minimize downtime and maximize yield. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*