federated-learning-homomorphic
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- Author repo skills-registry
- Domain
- Engineering
- Compatible agents
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- Claude Code
- Cursor
- Cline
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- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Manual integration
- External API key
- Required · Vendor-specific
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- Node.js · Python >=3.8
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Env read
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: federated-learning-homomorphic
description: This skill covers the implementation of privacy-preserving machine learning Use when this capabi…
category: engineering
runtime: Node.js / Python
---
# federated-learning-homomorphic output preview
## PART A: Task fit
- Use case: This skill covers the implementation of privacy-preserving machine learning Use when this capability is needed. def example_function(): | Type | Focus Area | Required Scenarios / Mocks | | :--- | :--- | :--- | | Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage | requires Vendor-specific API key; run….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Skill Profile / Overview / Why This Matters” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “This skill covers the implementation of privacy-preserving machine learning Use when this capability is needed. def example_function(): | Type | Focus Area | Required Scenarios / Mocks | | :--- | :--- | :--- | | Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage | requires Vendor-specific API key; run…”.
- **02** When the source has headings, the agent prioritizes “Skill Profile / Overview / Why This Matters” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.
## Running Rules
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands, read environment variables.
Validate with a small run
Start with a small task and check whether the result follows “Skill Profile / Overview / Why This Matters”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: federated-learning-homomorphic
description: This skill covers the implementation of privacy-preserving machine learning Use when this capabi…
category: engineering
source: tomevault-io/skills-registry
---
# federated-learning-homomorphic
## When to use
- This skill covers the implementation of privacy-preserving machine learning Use when this capability is needed. def ex…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Skill Profile / Overview / Why This Matters” and keep inference separate from source facts.
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "federated-learning-homomorphic" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Skill Profile / Overview / Why This Matters
rules -> SKILL.md triggers / order / output contract
runtime -> Node.js / Python | read files, write/modify files, run shell commands, read environment variables | may access external network resources
guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
# Example implementation following best practices
def example_function():
# Your implementation here
passAssumptions / 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.examplekeys: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
Source: AmnadTaowsoam/CerebraSkills — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review