On-device federated learning: collaborative training keeping raw data local while exchanging model updates; includes core APIs, FedAvg implementation, and Transport/Coordinator architecture roadmap.
SKaiNET is a Kotlin Multiplatform deep learning framework designed with a device-first philosophy and efficient, portable execution across JVM, Android and other targets. :contentReference[oaicite:0]{index=0}
This makes it a natural fit for on-device AI scenarios where models run close to the data instead of in a central cloud.
Federated learning extends this vision by allowing many devices to train a shared model collaboratively while keeping raw data local and private.
Traditional training:
Federated learning:
This matches SKaiNET’s goals of:
For a deeper dive into how SKaiNET handles federated learning, check out the Architecture Documentation. It covers:
We are currently in the initial development phase. The current focus is on building the Federated Core and standardizing aggregation strategies.
FederatedStrategy and model management.
SKaiNET is a Kotlin Multiplatform deep learning framework designed with a device-first philosophy and efficient, portable execution across JVM, Android and other targets. :contentReference[oaicite:0]{index=0}
This makes it a natural fit for on-device AI scenarios where models run close to the data instead of in a central cloud.
Federated learning extends this vision by allowing many devices to train a shared model collaboratively while keeping raw data local and private.
Traditional training:
Federated learning:
This matches SKaiNET’s goals of:
For a deeper dive into how SKaiNET handles federated learning, check out the Architecture Documentation. It covers:
We are currently in the initial development phase. The current focus is on building the Federated Core and standardizing aggregation strategies.
FederatedStrategy and model management.