Federated Learning Homomorphic

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

This skill covers the implementation of privacy-preserving machine learning techniques that enable training models on distributed, sensitive data without exposing raw data. It includes Federated Learning for distributed training, Homomorphic Encryption for computation on encrypted data, and Secure Multi-Party Computation (SMPC) for collaborative computation.

Why This Matters

• Data Privacy: Enables ML on sensitive data without privacy violations
• Regulatory Compliance: Meets GDPR, HIPAA, and other data protection regulations
• Collaborative Learning: Multiple organizations can collaborate without sharing data

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:
  • Local datasets on client devices (never transmitted to server)
  • Model architecture and hyperparameters
  • Privacy budget parameters (epsilon, delta)
  • Client selection criteria and participation requirements
• Entry Conditions:
  • TensorFlow Federated or PySyft installed
  • HE library (SEAL/TenSEAL) configured
  • Client devices with local data available
  • Secure communication channels established (TLS 1.3)
• Outputs:
  • Trained global model
  • Client participation metrics and contribution tracking
  • Privacy loss accounting reports
  • Model performance evaluation on test data
• Artifacts Required (Deliverables):
  • Federated training configuration
  • Client-side training scripts
  • Server aggregation logic
  • Privacy audit reports
• Acceptance Evidence:
  • Model achieves target accuracy without exposing raw data
  • Privacy budget not exceeded
  • Client contributions properly tracked
  • Communication overhead within acceptable limits
• Success Criteria:
  • Model accuracy within 5% of centralized training baseline
  • Privacy budget epsilon < 1.0 for DP-SGD
  • Communication overhead < 10x centralized training
  • Training completes within 2x centralized training time

Skill Composition

• Depends on: model-serving-inference, mlflow-patterns
• Compatible with: drift-detection-retraining, pii-policy-enforcement
• Conflicts with: None
• Related Skills: llm-security-redteaming

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)

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

Source: AmnadTaowsoam/CerebraSkills — distributed by TomeVault.