Federated Learning with Distributed Databases: Deep Dive into Implementation and Advanced Concepts
The preceding discussion established the foundational synergy between Federated Learning (FL) and distributed databases, highlighting their combined power for privacy-preserving, scalable AI. This continuation will delve deeper into implementation aspects, advanced concepts, and the evolving landscape of this transformative technological convergence.
Implementation Patterns: Bringing Theory to Practice
Implementing Federated Learning with distributed accurate cleaned numbers list from frist database databases involves strategic choices at both the client and server levels.
Client-Side Architecture:
Each client participating in the FL process effectively acts as a mini-AI ecosystem.
Local Data Store: For a large enterprise client with diverse data sources, a NoSQL database like Cassandra or MongoDB might be ideal due to their flexibility in handling varied data schemas (e.g., patient records, sensor data, transaction logs). For smaller edge devices or specialized applications, a lightweight distributed database like SQLite with replication or a simple key-value store might suffice. The choice depends on data volume, velocity, variety, and the computational resources available at the client.