A novel cryptographic approach to evaluate the security of federated learning frameworks

Federated Learning (FL) has turned out to be one of the most useful techniques for enabling distributed machine learning while protecting the data. Thirdly, because FL is distributed, the introduced feature is susceptible to several attacks associated with security. The current paper develops a new cryptographic solution that can enhance the security of FL frameworks. Thus, applying the cryptographic technologies, the employ of the proposed method ensures reliable data protection against the losses and illicit interventions. Experimental evaluations for this strategy are numerous and illustrate how, at the same time, our approach defends FL operations on the practical level while providing performance. It should also be noted how the results can provide a clear depiction to prove how beneficial our cryptographic solution will be in the context of improving the security of federated learning and paving the way for safer and more reliable FL implementations.