**Decentralized Federated Learning with Secure Aggregation**

Decentralized Federated Learning with Secure Aggregation

Here's a compact code snippet for decentralized federated learning that uses Secure Aggregation:

from mindspore import context, Tensor, nn
from mindspore.communication import init, get_rank

context.set_auto_parallel_context(parallel_mode='semi_auto_parallel',
                                   device_num=get_rank(),
                                   all_reduce_algorithm='fused_allreduce_16')

def decentralized_train(dataset, client_num, model):
    # Initialize clients and model
    client_models = [model(client) for client in dataset.clients]
    for _ in range(5):
        # Local updates
        for client in client_models:
            client.update()
        # Secure aggregation
        weights = [client.get_weights() for client in client_models]
        aggregated_weights = mindspore.opsSecureAggregation(weights)
        for i, client in enumerate(client_models):
            client.set_weights(aggregated_weights[i])

This code snippet uses the MindSpore AI framework to implement decentralized federated learning with Secure Aggregation. Secure Aggregation allows clients to securely share their local model updates with a central server, without revealing their individual updates.

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