on-device-model-training

name	on-device-model-training
description	On-Device Model Training enables training and updating machine learning Use when this capability is needed.
metadata	{"author":"amnadtaowsoam"}

On Device Model Training

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

On-Device Model Training enables training and updating machine learning models directly on edge devices without transferring raw data to the cloud. This approach is essential for privacy-sensitive applications, reduces bandwidth costs, enables personalized models, and provides continuous learning capabilities even in offline environments.

Why This Matters

• Data Privacy: Raw data never leaves the device, ensuring compliance with privacy regulations
• Bandwidth Efficiency: Only model updates (gradients) are transmitted, reducing bandwidth costs by 95-99%
• Personalization: Models adapt to individual user patterns, improving user experience
• Offline Learning: Continuous improvement without connectivity, enabling edge AI in remote locations
• Regulatory Compliance: Meets GDPR, HIPAA, and other data protection requirements by design

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Initial global model (PyTorch/TensorFlow)
  • Client configuration (server URL, training parameters)
  • Local data on client devices
  • Privacy parameters (epsilon, delta, noise scale)
• Entry Conditions:
  • Server deployed and accessible
  • Initial global model trained and available
  • Client devices have sufficient compute/memory for local training
  • Network connectivity between clients and server
  • Privacy parameters configured and documented
• Outputs:
  • Updated global model (aggregated from client updates)
  • Model version tracking
  • Training metrics (accuracy, loss, client participation)
  • Privacy budget tracking
• Artifacts Required (Deliverables):
  • Federated learning server implementation
  • Client training implementation
  • Privacy protection modules
  • Configuration files (server, client)
  • Monitoring and logging infrastructure
• Acceptance Evidence:
  • Global model converges to target accuracy (> 90% of centralized)
  • Privacy budget respected (epsilon < 5% per round)
  • Client participation rate > 70%
  • Round completion time < 5 minutes
  • Update success rate > 95%
• Success Criteria:
  • Model accuracy within 90-95% of centralized training
  • Privacy guarantees maintained (differential privacy, secure aggregation)
  • Bandwidth reduction > 95% vs data upload
  • Client participation > 70% of active clients
  • Round completion < 5 minutes
  • Update success rate > 95%

Skill Composition

• Depends on: tinyml-microcontroller-ai (Edge inference), edge-model-compression (Model optimization)
• Compatible with: hybrid-inference-architecture, edge-ai-development-workflow
• Conflicts with: None
• Related Skills: drift-detection-retraining, model-registry-versioning

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Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Type	Focus Area	Required Scenarios / Mocks
Unit	Core Logic	Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
Integration	DB / API	All external API calls or database connections must be mocked during unit tests
E2E	User Journey	Critical user flows to test
Performance	Latency / Load	Benchmark requirements
Security	Vuln / Auth	SAST/DAST or dependency audit
Frontend	UX / A11y	Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure

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