Sedna is an edge-cloud synergy AI project incubated in KubeEdge SIG AI. Benefiting from the edge-cloud synergy capabilities provided by KubeEdge, Sedna can implement across edge-cloud collaborative training and collaborative inference capabilities, such as joint inference, incremental learning, and federated learning. Sedna supports popular AI frameworks, such as TensorFlow, Pytorch, PaddlePaddle, MindSpore.
Sedna is an edge-cloud synergy AI project incubated in KubeEdge SIG AI. Benefiting from the edge-cloud synergy capabilities provided by KubeEdge, Sedna can implement across edge-cloud collaborative training and collaborative inference capabilities, such as joint inference, incremental learning, federated learning, and lifelong learning. Sedna supports popular AI frameworks, such as TensorFlow, Pytorch, PaddlePaddle, MindSpore.
Sedna can simply enable edge-cloud synergy capabilities to existing training and inference scripts, bringing the benefits of reducing costs, improving model performance, and protecting data privacy.
@@ -20,8 +20,9 @@ Sedna has the following features：
* Provide edge-cloud synergy training and inference frameworks.
* Joint inference: under the condition of limited resources on the edge, difficult inference tasks are offloaded to the cloud to improve the overall performance, keeping the throughput.
* Incremental training: For small samples and non-iid data on the edge, models can be adaptively optimized on the cloud or edge. The more the models are used, the smarter they are.
* Incremental training: For small samples and non-iid data on the edge, models can be adaptively optimized over time on the cloud or edge.
* Federated learning: For those scenarios that the data being too large, or unwilling to migrate raw data to the cloud, or high privacy protection requirements, models are trained at the edge and parameters are aggregated on the cloud to resolve data silos effectively.
* Lifelong Learning: Confronted with the challenge of heterogeneous data distributions in complex scenarios and small samples on the edge, the edge-cloud synergy lifelong learning: 1) leverages the cloud knowledge base which empowers the scheme with memory ability, which helps to continuously learn and accumulate historical knowledge to overcome the catastrophic forgetting challenge. 2) is essentially the combination of another two learning schemes, i.e., multi-task learning and incremental learning, so that it can learn unseen tasks with shared knowledge among various scenarios over time.
Sedna是在KubeEdge SIG AI中孵化的一个边云协同AI项目。得益于KubeEdge提供的边云协同能力，Sedna可以实现跨边云的协同训练和协同推理能力，如联合推理、增量学习、联邦学习等。Sedna支持目前广泛使用的AI框架，如TensorFlow/Pytorch/PaddlePaddle/MindSpore等，现有AI类应用可以无缝迁移到Sedna, 快速实现边云协同的训练和推理，可在降低成本、提升模型性能、保护数据隐私等方面获得提升。
Sedna是在KubeEdge SIG AI中孵化的一个边云协同AI项目。得益于KubeEdge提供的边云协同能力，Sedna可以实现跨边云的协同训练和协同推理能力，如联合推理、增量学习、联邦学习、终身学习等。Sedna支持目前广泛使用的AI框架，如TensorFlow/Pytorch/PaddlePaddle/MindSpore等，现有AI类应用可以无缝迁移到Sedna, 快速实现边云协同的训练和推理，可在降低成本、提升模型性能、保护数据隐私等方面获得提升。
Jie Pu