The AISHE servers aggregate data using a method known as federated learning. This approach allows multiple data providers to train machine learning models without the need to pool their data centrally.
Here’s how it works:
- Local Model Training: Each data provider trains a model on its own local dataset. This ensures that sensitive data remains secure and is not shared with other parties.
- Model Updates Sharing: Instead of sharing the raw data, each provider sends only the updates or improvements made to their local model to a central server.
- Central Aggregation: The central server receives these model updates from various providers and aggregates them to create a new, improved global model. This aggregated model incorporates insights from all participating data sources while maintaining data privacy.
- Feedback Loop: The updated global model is then sent back to the individual providers, allowing them to continue training on their local datasets with the latest improvements.
This federated learning process enhances the efficiency of data aggregation while ensuring privacy and security, making it particularly suitable for environments like financial trading where data sensitivity is crucial
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| How do the AISHE servers aggregate data |