Resilient Privacy-Preserving Collaborative Predictive Maintenance through a Federated Multi-Layer Architecture

The adoption of predictive maintenance in Industry 4.0 environments generates valuable operational data that could benefit from collaborative learning across multiple industrial facilities. However, competitive concerns and regulatory frameworks such as GDPR and NIS2 make centralized data aggregation increasingly impractical and non-compliant. Federated learning enables decentralized collaboration but requires confidentiality guarantees during model aggregation, and transparent governance mechanisms for multi-party trust. This paper proposes a resilient architecture integrating federated learning with trusted execution environments to protect aggregation processes, and blockchain for immutable governance, automatic model versioning, and operational continuity under adversarial or fault conditions. Operating across edge-fog-cloud tiers, the system enables industrial sites to collectively train models without sharing raw data. We present a formal system design with analysis of privacy guarantees, governance mechanisms, and fault-resilience properties - ensuring that neither a compromised sensor nor a corrupted model halts production operations. Analytical evaluation demonstrates that the security stack introduces a 123% latency overhead over standard federated averaging, remaining within acceptable bounds for daily or weekly industrial training cycles, while communication overhead remains below 1% of total traffic.