A Privacy Preserving Hybrid Deep Learning Framework With Block Chain Anchored Federated Training And Explainable Reasoning For Predictive Analytics In Industrial IoT

Authors: Asha Rani, Mukesh Singla

Abstract: Industrial Internet of Things (IIoT) deployments now stream terabyte-scale telemetry from programmable controllers, vibration sensors, smart meters and edge gateways every day. Predictive analytics on this data for fault diagnosis remaining useful life estimation, energy optimisation and anomaly screening has become a core operational requirement rather than a research curiosity. Yet the prevailing pattern of shipping raw plant data to a central cloud for model training exposes operators to data exfiltration, model-poisoning, regulatory penalties under GDPR and emerging EU AI Act obligations, and the growing class of adversarial perturbation attacks documented in 2024–2025 IIoT security literature. This paper proposes a layered framework that pairs a CNN–LSTM feature extractor with an Adaptive Neuro-Fuzzy Inference System (ANFIS) decision module, distributes training across edge nodes through a FedProx-based federated protocol with client-side differential privacy, anchors model-update integrity on a permissioned blockchain (Hyperledger Fabric with PBFT consensus), and surfaces decision rationale through SHAP attributions and ANFIS rule traces. The architecture targets the four properties that recent IIoT studies identify as gating industrial adoption: predictive accuracy, data confidentiality, tamper-evident auditability, and human-readable explanations. The paper articulates the design rationale, layer-wise responsibilities, expected performance envelope, and the trade-offs that practitioners must weigh between privacy guarantees, communication overhead, and latency on resource-constrained edge hardware.

DOI: http://doi.org/10.5281/zenodo.20536923