### Problem: Federated Learning
In this problem, we will implement federated learning to train a model on a decentralized dataset.
**Example:** Consider a dataset that is distributed across multiple clients. We want to train a model on this dataset without sharing the data with each other.
**Constraints:** Use NumPy to generate random data and labels. Implement the Federated Averaging algorithm to update the model parameters.
Test Cases
Test Case 1
Input:
[[0.5, 0.5], [0.5, 0.5]]Expected:
[0.5, 0.5]Test Case 2
Input:
[[0.2, 0.2], [0.2, 0.2]]Expected:
[0.2, 0.2]+ 3 hidden test cases