Secured cost-effective anonymous federated learning with proxied privacy enhancement for personal devices

Privacy concerns have escalated due to companies’ misuse of user data and the occurrence of data breaches and leaks worldwide. Uploading personal data from personal devices to a central server over the network poses a danger in obtaining an inference. Hence, a different approach is needed for this scenario. Federated learning enables collaborative training on devices while maintaining the privacy of user data. Federated learning originally aimed to address privacy concerns but is vulnerable to certain privacy attacks. Although certain privacy-enhancing strategies are available, researchers are actively seeking a more effective option. This research suggests two privacy improvement methods using proxies as a better option for personal devices in a federated learning environment, achieving good performance and cost effective without accuracy loss. We studied and assessed how the methodology compared to other methodologies. Finally, we discussed how this proposed technique can address the limitations of other techniques and possible collaborations with them.