highlight the developed part in the architecture

Signed-off-by: Marchons <[email protected]>

docs/proposals/algorithms/federated-class-incremental-learning/Federated Class-Incremental and Semi-Supervised learning Proposal.md

@@ -101,17 +101,13 @@ 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-20240729150049512](FCI_SSL_image/federated_learning_architecture)
-
-
+![image-20240909194418085](FCI_SSL_image/federated_learning_architecture)
 
 It is quite clear and specific that the paradigm need to start the server and client in single node and then perform federated learning, the whole process can be shown as follow:
 
+![image-20240909194832334](FCI_SSL_image/paradigm_process_2)
 
-
-![image-20240729150728176](FCI_SSL_image/paradigm_process_2)
-
-
+Federated Learning Paradigm involved above process, `the yellow block` means the process that the paradigm will execute, `the green block` is the process that paradigm will invoke the estimator to execute and `the pink block` is the process that paradigm will invoke the aggregation to execute.
 
 Base on the above paradigm, we can conduct our federated-class-incremental learning paradigm and the timing diagram of the entire Benchmarking system is presented below: 
 
@@ -135,22 +131,16 @@ We will leverage the existed *TestEnvManager*, *TestCaseController* and *StoryMa
 
 The overall architecture is shown as follow: 
 
-
-
-![image-20240821171243966](FCI_SSL_image/architecture_design)
-
-
+![image-20240909194437318](FCI_SSL_image/architecture_design)
 
 We design a novel algorithm paradigm namely Federated-Class-Incremental-Learning Paradigm,  We specify the process of the algorithm paradigm as follows:
 
-![image-20240729151355604](FCI_SSL_image/paradigm_process)
-
+![image-20240909194458903](FCI_SSL_image/paradigm_process)
 
+Federated Class Incremental Learning Paradigm involved above process, `the orange block` means the process that the paradigm will execute, `the green block` is the process that paradigm will invoke the estimator to execute and `the pink block` is the process that paradigm will invoke the aggregation to execute.
 
 In order to provide functionality extensibility and convenience to users, we have specified a process where most of the functionality can be replaced by user-implemented functionality(block in yellow). In addition, we require users to implement the server and client modules （block in green and orange） to complete the whole algorithm process. 
 
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 ### 3.3 Benchmarking Design 
 
 **Datasets Setting**  
@@ -393,9 +383,9 @@ ianvs -f ./example/cifar100/federated_learning/fedavg/benchmarkingjob.yaml
 
 #### 3.5.2 Federated Class Incremental Learning
 
-Base on the architecture above, If user want to implement a federated class incremental learning algorithm， they can following the below instruction:
+Base on the architecture above, If user want to implement a federated class incremental learning algorithm，they can following the below instruction:
 
-example path: `example/cifar100/federeated_class_incremental_learning/glfc`
+example path: `example/cifar100/federeated_class_incremental_learning/fedavg`
 
 1. Define an `Estimator` but you can choose to implement a helper function
 
@@ -499,6 +489,8 @@ ianvs -f ./example/cifar100/federeated_class_incremental_learning/glfc/benchmark
 
 To match the implementation of federated learning in Sedna, if user want to implement a federated learning algorithm, they can following the below instruction:
 
+The different between this example and example 3.5.1 is that in this version, client and server communicate through websocket while example 3.5.1 communicate with in memory.
+
 example path: `example/cifar100/sedna_federeated_learning/fedavg`
 
 1. Define an  `Estimator` like `3.5.1 Federated Learning Example `