How does federated learning work within the AISHE system

The AISHE system implements federated learning to enable collaborative AI model training while preserving data privacy. Here's how federated learning works within the AISHE system:

Decentralized Data Processing

Data remains on local devices or servers of AISHE system clients, rather than being centralized.
Each client trains the model on their private data locally.

Model Sharing and Aggregation

Clients download a pre-trained model from the AISHE cloud servers.
After local training, clients encrypt and send only the model updates back to the central server.
The AISHE server decrypts, averages, and integrates these updates into the central model.

Iterative Improvement

This process of local training and central aggregation repeats iteratively.
The collaborative effort gradually improves the global model without sharing raw data.

Privacy Preservation

The AISHE system employs differential privacy techniques to ensure individual data cannot be traced back to participants.
Secure communication protocols encrypt all data exchanges between clients and servers.

By leveraging federated learning, the AISHE system enables financial institutions and traders to benefit from collective intelligence while maintaining data confidentiality and regulatory compliance.