Improving Safe Cooperation in AI Models by Means of Federated Transfer Learning Systems

Abstract

Federated learning (FL) methods have been driven in artificial intelligence (AI) model training by the growing need for safe, cooperative learning over dispersed networks. Though federated learning guarantees privacy by distributing data, the difficulty in improving the security of model sharing and cooperation—especially in hostile environments—remains. By letting pre-trained models be adapted across many contexts, transfer learning (TL) presents a special method that helps to improve the performance of the model with less data needs. Focusing on reducing the risks related with data leakage, model poisoning, and malicious assaults, this study explores how federated learning, when coupled with transfer learning techniques, might improve safe cooperation across distant artificial intelligence models. The current work offers a thorough investigation of federated transfer learning (FTL), its interaction with safe artificial intelligence systems, and the difficulties in its use. Existing implementation case studies show how well these systems lower security concerns and improve model accuracy. Moreover, the work investigates the future possibilities of federated transfer learning for improving cooperative artificial intelligence without endangering data security and privacy.