Our STADLE platform is a paradigm-shifting technology for adaptive and continuous learning combining privacy-preserving Machine Learning (ML) and decentralized system capability to provide scalable, versatile, and secure AI services. STADLE stands for Scalable & Traceable AI platform for Distributed LEarning.
Federated Learning (FL) solves the problems of privacy and communication load, which commonly appear in ML systems. FL does not require users to upload raw data to cloud servers.
- Privacy: FL improves the privacy-preserving aspect of AI systems by not collecting data in the cloud while producing collective intelligence based on uploaded user ML models.
- Communication load: The amount of traffic generated by FL dramatically decreases from classical AI systems due to the difference in data type exchanged.
Our STADLE platform enhances the capability of FL by incorporating decentralized architecture.
- Scalability: Decentralized FL servers in STADLE realizes the load-balancing to accommodate more users.
- 5G-friendliness: The delay in communication to obtain collective intelligence can be dramatically reduced by employing decentralized FL servers located at edge servers.
- Traceability: Our platform has the performance tracking capability that monitors and manages the transition of collective intelligence models in the decentralized system.
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For additional guides, examples, and API’s see the documentation.