# Service: Federated Learning Architecture

## metadata
- title: Federated Learning Service | Klyff
- route: /services/federated-learning
- timeline: 12-16 weeks multi-site architecture
- focus: Privacy-preserving cross-plant intelligence

## Overview
Klyff designs and deploys distributed machine learning architectures where independent manufacturing sites train models locally on proprietary sensor data, aggregating intelligence without ever centralizing raw files.

## Performance Benchmarks
- **Deployment Window:** Multi-site network operational in 12-16 weeks.
- **Data Transfer Costs:** Zero bandwidth or cloud egress expenses for video/sensor streams.
- **Governance Simplicity:** 50%+ reduction in international data compliance overhead.
- **Convergence Speed:** 40%+ faster accuracy gains compared to isolated single-plant models.

## What We Do

### 1. Multi-Site Distributed Architecture Design
- Designs robust topologies where each factory runs local inference and training on its own edge servers, connected to a secure central model orchestrator.
- Supports asynchronous model aggregation so that network drops or slower lines do not stall fleet-wide model updates.
- Scales across dozens of worldwide plants without creating expensive centralized cloud data lakes.

### 2. Privacy-Preserving Aggregation Setup
- Each site trains locally and transmits only encrypted weight updates (gradients) to the central aggregator.
- Integrates differential privacy algorithms to mathematically guarantee that raw production data or proprietary defect images cannot be reverse-engineered.
- Satisfies international data sovereignty mandates (GDPR, CCPA) and enterprise intellectual property protection.

### 3. Per-Site Model Customization & Adaptation
- While the global federated model provides a generalized backbone, each plant fine-tunes local adapter weights for unique line speeds, equipment revisions, or local defect types.
- Manages model version control to ensure local fine-tuning improves line accuracy without degrading the global model.

### 4. Automated Federated Training Cycles
- Orchestrates recurring rounds: local training on new factory shifts, gradient upload, weighted federated averaging, and distribution of the upgraded model back to lines.
- Enforces strict versioning, audit logging, and automated rollback capabilities essential for manufacturing quality certifications.

### 5. Continuous Optimization & Drift Monitoring
- Tracks performance across all plants over 3-6 months, identifying local data drift caused by tooling wear, ambient lighting, or material batches.
- Drives ongoing 5-15% accuracy gains by leveraging edge discoveries across the entire factory fleet.

## Engagement Model
1. **Weeks 1-2:** Distributed topology and privacy architecture design.
2. **Weeks 3-8:** Edge training orchestration deployment across pilot plants.
3. **Weeks 9-12:** Federated aggregation cycles and local adapter tuning.
4. **Months 3-6:** Continuous fleet monitoring and multi-site optimization reviews.