Agentic Design

Patterns
🌐

Local-Distant Agent Data Protection Pattern(LDADP)

Distributed agentic architecture combining local processing agents with distant aggregation agents using advanced anonymization techniques for privacy-preserving AI

Complexity: highSecurity & Privacy Patterns

🎯 30-Second Overview

Pattern: Distributed architecture where local agents process sensitive data on-device while distant agents coordinate federated learning through advanced anonymization and differential privacy

Why: Enables multi-party AI collaboration without exposing raw sensitive data, meeting regulatory compliance while maintaining model effectiveness

Key Insight: Local isolation + differential privacy + federated aggregation → privacy-preserving agentic AI with <0.001% re-identification risk

⚡ Quick Implementation

1Local Agent Setup:Deploy on-device processing agents with data isolation
2Privacy Mechanisms:Implement differential privacy and anonymization
3Distant Aggregator:Configure federated learning coordination agent
4Secure Communication:Establish encrypted channels between agents
5Privacy Verification:Validate anonymization and privacy guarantees
Example: local_agent_setup → privacy_mechanisms → distant_aggregator → secure_communication → privacy_verification

📋 Do's & Don'ts

✅Keep raw sensitive data isolated on local processing agents
✅Apply differential privacy noise before any data transmission
✅Implement individualized privacy budgets per agent
✅Use secure multi-party computation for aggregation
✅Verify privacy guarantees before accepting model updates
❌Transmit raw data between local and distant agents
❌Use fixed privacy parameters across all agents
❌Skip anonymization verification for model updates
❌Allow direct communication between local agents
❌Ignore privacy budget depletion warnings

🚦 When to Use

Use When

  • • Multi-party sensitive data collaboration
  • • Regulatory compliance requirements (HIPAA, GDPR)
  • • Cross-organizational AI development
  • • Privacy-critical applications

Avoid When

  • • Single-organization deployments
  • • Public dataset processing
  • • Real-time low-latency requirements
  • • Non-sensitive data applications

📊 Key Metrics

Privacy Budget Usage
% of epsilon consumed across all agents
Re-identification Risk
Probability of data re-identification (<0.001% target)
Model Accuracy Trade-off
% accuracy vs centralized approach
Local Processing Latency
On-device computation time (ms)
Federated Convergence
Rounds needed for model convergence
Communication Overhead
% reduction vs raw data sharing

💡 Top Use Cases

Healthcare: Multi-hospital federated learning with patient data isolation and differential privacy
Finance: Cross-bank fraud detection with transaction anonymization and secure aggregation
Smart Cities: Traffic optimization using citizen data with local processing and privacy preservation
Manufacturing: Industrial IoT collaboration with proprietary data protection and federated insights
Research: Multi-institution studies with sensitive data anonymization and privacy-preserving analysis

References & Further Reading

Deepen your understanding with these curated resources

Federated Learning & Differential Privacy

Differentially Private Federated Learning: A Systematic Review (ArXiv 2024)

Federated Learning with Formal Differential Privacy Guarantees (Google Research)

Adaptive Differential Privacy in Federated Learning (ArXiv 2024)

Using Decentralized Aggregation for Federated Learning with Differential Privacy

Privacy-Preserving Techniques

AI-Driven Anonymization: Protecting Personal Data Privacy (ArXiv 2024)

Federated Learning with Individualized Privacy Through Client Sampling

Advanced Artificial Intelligence with Federated Learning (Scientific Reports 2025)

Enhancing Convergence of FL Aggregation Strategies with Limited Data

Agentic AI Systems

AgentAI: Comprehensive Survey on Autonomous Agents (ScienceDirect 2024)

The Role of Agentic AI in Shaping Smart Future (ScienceDirect 2025)

Agentic AI, Edge AI and Distributed Intelligence (RCR Wireless)

Learn Agentic AI using Dapr Cloud Design Patterns (GitHub)

Regulatory & Compliance

TechDispatch Federated Learning (European Data Protection Supervisor 2025)

Minding Mindful Machines: AI Agents Data Protection (Future of Privacy Forum)

Federated Learning for Breast Cancer Diagnosis (Scientific Reports 2025)

Overview of Security and Privacy in Federated Learning (Artificial Intelligence Review)

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