Federated Learning for Multi-Site Intelligence
Privacy-preserving AI across multiple factories
Problem Statement
Large manufacturers with multiple sites want to learn collectively from each facility’s data. But data privacy, governance, and security concerns make centralized data lakes impractical. Federated learning is the solution.
Solution
Klyff federated learning enables each site to train locally on its own data, then aggregate models without sharing raw data. Collective intelligence + complete privacy
Zero
Data movement costs
50%+
Reduction in data governance complexity
40%+
Faster model improvement (vs. single-site)
100%
Full GDPR/HIPAA compliance
How It Works
PCB Assembly Sites
Learning optimal inspection across multiple plants
Automotive manufacturing
Defect detection standardization
Food & beverage
Quality control across regions
Pharmaceutical
Compliance across facilities
Case Study
Company: Global EMS Manufacturer
Industry: Electronics Contract Manufacturing (8 global facilities)
Challenge: Â Inconsistent defect detection across sites; data privacy concerns with centralized ML
Solution: Klyff federated learning across 8 manufacturing sites
Results:
- 35% improvement in defect detection accuracy (collective learning)
- $0 data movement costs (vs. $50K/month with cloud)
- Full data sovereignty (zero data leaves each facility)
- Standardized quality metrics across regions
“With the help of Klyff we now have centralized learning over our 8 manufacturing sites. This collective intelligence with privacy control made sure that we are not in the dark with important decisions”
— John Miller, Quality Director, EMS Manufacturing
Selected Customer Success Stories
Adaptive Predictive Maintenance
Solder Joint Inspection
PPE Compliance in Chemical Plant
Our Insights to keep you ahead
A Comprehensive Guide to Edge AI in Manufacturing
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