Federated Learning for Privacy-Preserving Predictive Maintenance in Industrial IoT Manufacturing Systems

The integration of Industrial Internet of Things (IIoT) sensors and deep learning algorithms has catalyzed predictive maintenance (PdM) paradigms, enabling precise Remaining Useful Life (RUL) estimations across critical industrial assets. However, conventional centralized deep learning frameworks necessitate the aggregation of massive telemetry streams on remote cloud servers, exposing proprietary operational data, manufacturing configurations, and intellectual property to adversarial interception and non-compliance with data governance mandates. In this article, we propose an adaptive, privacy-preserving federated learning (FL) framework tailored for PdM across heterogeneous, multi-facility manufacturing environments. Our framework couples client-side temporal convolutional networks integrated with bidirectional long short-term memory (TCN-BiLSTM) units and a dynamic aggregation protocol based on proximal regularization to mitigate client drift induced by non-IID (non-independently and identically distributed) machine telemetry. Furthermore, localized differential privacy mechanisms and gradient compression techniques are embedded to safeguard model updates against membership inference while preserving communication bandwidth across resource-constrained edge gateways. Extensive empirical evaluations conducted on real-world industrial machinery benchmarks demonstrate that our federated framework achieves an RUL estimation accuracy within 1.8% of a fully centralized model while decreasing network transmission payloads by over 68% and providing formal (ε, δ)-differential privacy guarantees. This study presents a resilient and scalable blueprint for decentralized artificial intelligence deployment in collaborative Industry 4.0 ecosystems.