Figure 1.
Blockchain-Enabled Federated Deep Learning (BFDL) Framework for Privacy-Preserving Social Media Content Moderation.
Figure 2.
Bi-LSTM with attention mechanism.
Figure 3.
Functional Role of the Blockchain Layer in Federated Deep Learning.
Table 1.
List of simulation parameters.
| Parameter | Symbol / Value | Description |
|---|---|---|
| Number of Clients | (N-20) | Number of simulated edge participants involved in federated learning. Each client locally trains the model using its own data without directly sharing raw social media data. |
| Communication Rounds | (T=50) | Maximum number of global federated communication rounds. In each round, selected clients train locally and transmit model updates to the central aggregation process. |
| Local Training Epochs | Maximum = 50 | Maximum number of local training epochs permitted for each client. The actual number of epochs is determined adaptively using early stopping. |
| Early Stopping Metric | Validation Loss | Validation loss is monitored during local training to determine whether further training improves model generalization. |
| Early Stopping Patience | 10 epochs | Training is stopped when validation loss does not improve for 10 consecutive epochs. |
| Learning Rate | (η =0.001) | Fixed learning rate used for local gradient-based optimization. It provides a stable and reproducible optimization setting across participating clients. |
| Batch Size | 64 | Number of training samples processed in one local optimization iteration. |
| LSTM Hidden Units | 128 | Number of hidden neurons used in each Bi-LSTM representation layer for learning temporal dependencies in social media data. |
| LSTM Architecture | Bi-LSTM | Bidirectional LSTM is used to capture contextual dependencies from both forward and backward directions in sequential social media representations. |
| Attention Dimension | 64 | Dimension of the attention context representation used to assign greater importance to informative features or sequence elements. |
| Optimizer | Adam | Adaptive Moment Estimation optimizer used for efficient local model parameter optimization. |
| Encryption / Hashing | SHA-256 | SHA-256 cryptographic hashing is used to provide integrity verification and secure identification of transmitted model-update information. |
| Trust Threshold | (τ =0.6) | Minimum trust score required for a client to be considered reliable for participation in model aggregation. |
| Malicious Client Identification | Trust Score (<0.6) | Clients whose trust score falls below the predefined threshold are treated as potentially unreliable or malicious participants. |
| Random Seed | 42 | Fixed seed used for reproducibility of stochastic operations such as model initialization, data shuffling, and experimental execution. |
| Number of Experimental Runs | 10 | Independent repetitions recommended for statistical reliability and repeatability analysis. |
| Statistical Measures | Mean ± SD | Mean and standard deviation are reported across repeated runs to quantify average performance and variability. |
| Confidence Level | 95% CI | A 95% confidence interval can be reported to quantify uncertainty around the mean performance. |
| Maximum Global Rounds | 50 | Upper bound on global aggregation iterations; the model performance is evaluated across the federated communication process. |
Figure 4.
Loss vs. Epochs.
Figure 5.
Confusion matrix.
Figure 6.
Performance metric.
Table 2.
Statistical and Repeatability Analysis of the Proposed Trust-Aware Federated Learning Model Across Multiple Experimental Runs.
| Phase | Metric | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Run 6 | Run 7 | Run 8 | Run 9 | Run 10 | Mean ± SD | 95% CI |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Training | Accuracy (%) | 97.06 | 97.18 | 96.94 | 97.11 | 97.02 | 97.15 | 96.89 | 97.09 | 97.13 | 96.98 | 97.06 ± 0.09 | 97.00-97.12 |
| Training | Precision (%) | 96.79 | 96.91 | 96.68 | 96.84 | 96.75 | 96.88 | 96.63 | 96.82 | 96.87 | 96.73 | 96.79 ± 0.09 | 96.73-96.85 |
| Training | Recall (%) | 97.16 | 97.25 | 97.08 | 97.21 | 97.11 | 97.19 | 97.03 | 97.14 | 97.23 | 97.06 | 97.15 ± 0.07 | 97.10-97.20 |
| Training | F1-score (%) | 96.98 | 97.08 | 96.88 | 97.02 | 96.93 | 97.04 | 96.82 | 96.99 | 97.05 | 96.89 | 96.97 ± 0.08 | 96.91-97.03 |
| Validation | Accuracy (%) | 95.75 | 95.88 | 95.61 | 95.82 | 95.69 | 95.79 | 95.55 | 95.73 | 95.84 | 95.65 | 95.73 ± 0.10 | 95.66-95.80 |
| Validation | Precision (%) | 95.38 | 95.51 | 95.24 | 95.45 | 95.32 | 95.42 | 95.18 | 95.36 | 95.47 | 95.28 | 95.36 ± 0.10 | 95.29-95.43 |
| Validation | Recall (%) | 95.88 | 96.01 | 95.74 | 95.95 | 95.82 | 95.92 | 95.68 | 95.86 | 95.97 | 95.78 | 95.86 ± 0.10 | 95.79-95.93 |
| Validation | F1-score (%) | 95.63 | 95.76 | 95.49 | 95.70 | 95.57 | 95.67 | 95.43 | 95.61 | 95.72 | 95.53 | 95.61 ± 0.10 | 95.54-95.68 |
Figure 7.
Trust Score vs. Clients.
Figure 8.
Accuracy vs. number of malicious clients.
Figure 9.
ROC curve.
Figure 10.
Blockchain dynamics result.