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Federated Learning Pattern

A distributed learning pattern that enables multiple organizations or departments to collaboratively train AI models without sharing raw data. This pattern maintains data privacy and security while leveraging collective knowledge across organizational boundaries.

When to Use

Use this pattern when multiple parties want to benefit from shared AI models but cannot share sensitive data due to privacy, regulatory, or competitive concerns. Essential for healthcare consortiums, financial institutions, and cross-organizational collaborations where data governance is critical.

Architecture Drawing

The architecture shows multiple participating organizations with local training capabilities connected through a central aggregation server that coordinates model updates without accessing raw data.

Components: - Central Aggregation Server (400, 100) - Organization A Trainer (150, 200) - Organization B Trainer (400, 200) - Organization C Trainer (650, 200) - Local Data A (150, 300) - Local Data B (400, 300) - Local Data C (650, 300) - Global Model Registry (400, 50) - Security Controller (550, 100)

Edges: - Global Model Registry → Central Aggregation Server: "Base Model" - Central Aggregation Server → Organization A Trainer: "Global Model Updates" - Central Aggregation Server → Organization B Trainer: "Global Model Updates" - Central Aggregation Server → Organization C Trainer: "Global Model Updates" - Organization A Trainer → Central Aggregation Server: "Local Model Updates" - Organization B Trainer → Central Aggregation Server: "Local Model Updates" - Organization C Trainer → Central Aggregation Server: "Local Model Updates" - Local Data A → Organization A Trainer: "Training Data" - Local Data B → Organization B Trainer: "Training Data" - Local Data C → Organization C Trainer: "Training Data" - Security Controller → Central Aggregation Server: "Privacy Validation"

Privacy-Preserving AI Pattern, Multi-Tenant Architecture Pattern, Distributed Training Pattern

References

  1. Communication-Efficient Learning of Deep Networks from Decentralized Data - 2016 - arXiv - Foundational paper introducing federated learning and its communication-efficient algorithms
  2. Federated Learning: Challenges, Methods, and Future Directions - 2019 - arXiv - Comprehensive survey of federated learning challenges and solutions in practice
  3. Privacy-Preserving Federated Learning in Healthcare - 2021 - Nature Digital Medicine - Real-world applications of federated learning in healthcare with privacy considerations