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a/docs/proposals/algorithms/federated-class-incremental-learning/Federated Class-Incremental and Semi-Supervised learning Proposal.md +++ b/docs/proposals/algorithms/federated-class-incremental-learning/Federated Class-Incremental and Semi-Supervised learning Proposal.md @@ -103,7 +103,7 @@ Federated incremental learning is a special kind of federated learning. However Sedna is a edge-cloud collaborative training AI framework. It support federated learning ability. Base on Sedna, we can have a simple federated learning architecture: -![image-20240909194418085](FCI_SSL_image/federated_learning_architecture) +![image-20240912171642528](FCI_SSL_image/federated_learning_architecture) ##### Implementation Detail @@ -219,7 +219,7 @@ We will leverage the existed *TestEnvManager*, *TestCaseController* and *StoryMa The overall architecture is shown as follow: -![image-20240909194437318](FCI_SSL_image/architecture_design) +![image-20240912172517676](FCI_SSL_image/architecture_design) ##### Implementation Detail @@ -443,7 +443,7 @@ $$ #### 3.5.1 Federated Learning Example -Base on the architecture above, If user want to implement a federated learning algorithm, they can following the below instruction: +Base on the architecture above, If user want to implement a federated learning algorithm, ![image-20240912171635194](Federated Class-Incremental and Semi-Supervised learning Proposal.assets/image-20240912171635194.png)they can following the below instruction: example path: `example/cifar100/federeated_learning/fedavg`

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