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Federated Learning Frameworks
Federated Learning enables machine learning across distributed data sources without centralizing the data - preserving privacy while training global models.
Predictive Modeling & Machine Learning
In industries like healthcare, finance, and IoT, data can’t always be centralized due to privacy, regulation, or bandwidth. We design federated learning systems that train shared models across edge devices or partners, ensuring that sensitive data stays local while the system still learns collectively.
What we can do with it:
Build federated learning pipelines across hospitals, branches, or devices.
Design custom aggregation logic for secure model updates.
Ensure differential privacy and compliance with GDPR/HIPAA.
Deploy learning agents at the edge with lightweight models.
Enable cross-silo collaboration without data exchange.
Visualize participation metrics and convergence progress.
Implement rollback for corrupted or biased local updates.
Support heterogenous hardware and connection scenarios.
Integrate FL with IoT networks and mobile fleets.
Monitor overall system performance and fairness centrally.