I have recently been reading your article "Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI" and attempting to implement the described multi-class federated learning model. I greatly appreciate your work on leveraging blockchain and federated learning to enhance the security of Android applications.
During the implementation, I noticed that the article does not provide specific implementation details for the evaluation_fn function. I understand that this function is crucial for ensuring the performance evaluation of the model across different clients. Therefore, I would like to request you to share more information regarding the evaluation_fn function in binary_classification_server.py AND multiclass_classification_server.py, or provide reference implementation code for this function. To better reproduce and understand your article, I am in need of:
Specifics on the implementation of the evaluation function: If possible, provide the code for the evaluation_fn function.
Suggestions on data handling: Advice on how to process and partition the dataset.
This information would be greatly beneficial for my understanding and implementation of the model. Thank you very much for your valuable time, and I look forward to your response!
![Uploading 图片1.png…]()
I have recently been reading your article "Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI" and attempting to implement the described multi-class federated learning model. I greatly appreciate your work on leveraging blockchain and federated learning to enhance the security of Android applications.
During the implementation, I noticed that the article does not provide specific implementation details for the evaluation_fn function. I understand that this function is crucial for ensuring the performance evaluation of the model across different clients. Therefore, I would like to request you to share more information regarding the evaluation_fn function in binary_classification_server.py AND multiclass_classification_server.py, or provide reference implementation code for this function. To better reproduce and understand your article, I am in need of:
Specifics on the implementation of the evaluation function: If possible, provide the code for the evaluation_fn function.
![Uploading 图片1.png…]()
Suggestions on data handling: Advice on how to process and partition the dataset.
This information would be greatly beneficial for my understanding and implementation of the model. Thank you very much for your valuable time, and I look forward to your response!