Confronting the Threats of Federated Learning: ArKrum and Stochastic Gradient Descent's Accuracy Innovation Demonstrated in Large-Scale Networks

As the expansion of decentralized networks such as blockchain and IoT continues, countermeasures against attacks from malicious nodes are becoming an urgent priority. This time, a secure machine learning system combining ArKrum and differential privacy has established a new scalability benchmark.

Successful verification in a large-scale environment of 10 million nodes

The integrated approach of ArKrum and differentially private stochastic gradient descent (DP-SGD) achieved scaling up to 10 million nodes under the condition of a noise multiplier of 0.3. Compared to a 1 million node scale, there was a slight decrease in accuracy due to adjustment processing overhead, but maintaining an accuracy of 0.76 demonstrates its practicality in large-scale distributed environments.

Confirmed robustness under severe attack conditions

In the test, 20 rounds of training were conducted using the CIFAR-10 dataset, with a scenario where 30% of the nodes are malicious. Experiments through a distributed mock in PyTorch revealed that the system functions stably even in such a highly adversarial environment.

Clear path to the next stage

The future development roadmap includes integration with blockchain verification mechanisms and additional validation using the MNIST dataset. This will verify versatility across different data environments and blockchain settings, paving the way for practical application.