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What we propose:
- an edge-cloud collaborative AI framework based on KubeEdge
- with embed collaborative training and joint inferencing algorithm
- working with existing AI framework like Tensorflow, etc
3 Features：
- joint inference
- incremental learning
- federated learning
Targeting Users：
- Domain-specific AI Developers: build and publish edge-cloud collaborative AI services/functions easily
- Application Developers: use edge-cloud collaborative AI capabilities.
We are NOT:
- to re-invent existing ML framework, i.e., tensorflow, pytorch, mindspore, etc.
- to re-invent existing edge platform, i.e., kubeedge, etc.
- to offer domain/application-specific algorithms, i.e., facial recognition, text classification, etc.
Design Details
Architecture
GlobalCoordinator: implements the Edge AI features controllers based on the k8s operator pattern
- Federated Learning Controller: Implements the federated learning feature based on user created CRDs
- Incremental Learning Controller: Implements the incremental learning feature based on user created CRDs
- Joint Inference Controller: Implements the joint inference feature based on user created CRDs
LocalController: manages the Edge AI features, the extra dataset/model resources on the edge nodes
Workers: includes the training/evaluation/inference/aggregator
- do inference or training, based on existing ML framework
- launch on demand, imagine they are docker containers
- different workers for different features
- could run on edge or cloud
Lib: exposes the Edge AI features to applications, i.e. training or inference programs