Federated Learning for Automotive Aftermarket Supply Chains: A Privacy-Preserving Framework for Predictive Maintenance Optimization

Global automotive aftermarket networks face critical challenges in predicting part failures while maintaining data privacy across decentralized suppliers and distributors. This article presents a novel federated learning framework that enables collaborative predictive maintenance without raw data sharing. The article combines edge-based LSTM networks for local failure prediction using IoT sensor data with a cloud-based meta-model aggregating knowledge via secure multi-party computation. Privacy preservation is achieved through differential privacy applied to gradient updates and homomorphic encryption for sensitive feature aggregation. Domain-specific optimizations include attention mechanisms for handling intermittent failure patterns and transfer learning across part categories. Validated across a network of Tier-1 suppliers and distribution centers, the framework achieves significant prediction accuracy improvements over isolated models, reduces unnecessary part replacements, and maintains full compliance with regulatory standards while optimizing inventory management across participants.