Reduced Communication Costs: more efficient than transferring entire datasets, especially for large-scale deployments or edge devices with limited bandwidth.
Access to Heterogeneous Data: FL can aggregate insights from diverse datasets residing in different locations, addressing data silos and enriching the overall model.
On-Device Learning: It facilitates training models directly accurate cleaned numbers list from frist database on edge devices (smartphones, IoT devices), enabling personalized experiences and reducing reliance on cloud infrastructure.
However, FL's success hinges on efficient management and accessibility of data on the client side. This is where the synergy with distributed databases becomes critical.
Distributed Databases: The Foundation for Decentralized Data Management
Distributed databases are systems where data is stored across multiple interconnected computers or nodes, often in different geographical locations. They offer several advantages over traditional monolithic databases:
Scalability: Data can be horizontally scaled by adding more nodes, accommodating ever-growing datasets and user bases.
Availability and Fault Tolerance: If one node fails, the system can continue operating, ensuring high availability and data resilience.