Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization

2026-04-30 · Source: cs.LG updates on arXiv.org · Field: Manufacturing & Industrial — Smart Manufacturing & Industry 4.0, Manufacturing Operations & Management, Materials & Production Technology · Depth: Expert, extended

Summary

A privacy-preserving federated learning (FL) framework has been developed for distributed chemical process optimization, enabling collaborative model training across geographically separated industrial plants without sharing raw operational data. Each plant locally trains a neural-network-based process model using its time-series sensor data, transmitting only encrypted model parameters to a central aggregation server. Experimental evaluation using datasets from three independent chemical plants demonstrated rapid convergence, with the global mean squared error (MSE) decreasing from approximately 2369 to below 50 within five communication rounds and stabilizing around 35 after 40 rounds. This federated approach significantly improved prediction accuracy across all plants compared to local-only training, achieving performance comparable to centralized training while maintaining strict data locality and industrial confidentiality.

Key takeaway

For AI Scientists and Research Scientists developing solutions for industrial IoT or chemical engineering, this framework demonstrates that federated learning can significantly enhance predictive model accuracy across distributed plants while strictly preserving data privacy. You should consider implementing secure aggregation and weighted averaging strategies to manage data heterogeneity and ensure robust model convergence in real-world, multi-plant deployments.

Key insights

Federated learning enables privacy-preserving, collaborative AI model training for distributed chemical process optimization.

Principles

• Data locality is paramount for industrial confidentiality.
• Parameter sharing enables cross-plant knowledge transfer.
• Weighted aggregation balances diverse plant contributions.

Method

The framework uses local neural network training, encrypted parameter transmission, and weighted aggregation at a central server to iteratively update a global model, ensuring data privacy.

In practice

• Apply FL for predictive monitoring in distributed plants.
• Use FL for anomaly detection in industrial IoT.
• Integrate FL with process control systems.

Topics

• Federated Learning Framework
• Chemical Process Optimization
• Privacy-Preserving AI
• Industrial IoT Systems
• Neural Network Modeling

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.