Federated Learning with Attention-Based Data Selection: Algorithm and Implementation

This document outlines an algorithm for federated learning with attention-based data selection. The algorithm aims to improve model performance by selectively incorporating data points based on their relevance and influence. The key steps are described below:

Algorithm:

Initialize local models with parameters w_k for each client k.
For each client k do: 3. For each data point i do: 4. Calculate the attention score A_ikj for data point i, client k, and data point j on other clients. 5. Update local model parameters w_k using the attention scores and the local data. r_t + γ * max(a * Q(s'_ij, a; w_k) - Q(s_ij, a_t; w_k)); 10. Update Q(s_ij, a_t; w_k) ← Q(s_ij, a_t; w_k) + αδ; 11. Update attention scores A_ikj ← A_ikj + η |δ|; 6. Send local model parameters w_k and attention scores A_ikj to Central Server. 7. End for
For each data point i do: 4. Calculate the weighted sum of attention scores P_k * A_ikj / K for all clients k. 5. If P_k * A_ikj / K < θ, reduce the influence of data point i in the global model. 6. End for
Aggregate local model parameters to update global parameters:
```
W ← P_k * w_k;
```
End for

Explanation:

Step 2-5: Each client trains a local model on its data. Attention scores are calculated to assess the relevance of data points on other clients to the local model. This score is used to update local model parameters, effectively weighting the influence of data points from other clients.
Step 6: Local models and attention scores are sent to the central server.
Step 3-5: The central server analyzes the attention scores to identify data points with low influence and reduces their impact on the global model. This step ensures that only relevant data points contribute to the final model.
Step 7: The central server aggregates local model parameters to update the global model, taking into account the reduced influence of irrelevant data points.

This algorithm incorporates attention-based data selection to improve the performance of federated learning by selectively incorporating relevant data points. The algorithm helps address challenges related to data heterogeneity and privacy in federated learning environments.

Federated Learning with Attention-Based Data Selection: Algorithm and Implementation