Research Article
Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN + BiLSTM + LSTM Model
Algorithm 1
Federated averaging learning procedure with TLS encryption.
| Input: number of rounds T, number of local epochs E, and number of participating clients N | | Output: final global model W after T rounds of federated learning | (1) | Initialize: Global model ; | (2) | for t = 1 to T do | (3) | for i = 1 to N do | (4) | Establish a secure TLS connection between the client and the server; | (5) | Retrieve local data and associated labels | (6) | Initialize: Local model parameters = ; | (7) | for e = 1 to E do | (8) | Update using and through local training; | (9) | end | (10) | Securely send the updated local model to the server using the established TLS connection; | (11) | end | (12) | Aggregate and update the global model using the received local models; | (13) | Send the updated global model to all participating clients using the TLS connection; | (14) | end |
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