What question did this study set out to answer?

The study aims to develop a privacy-preserving recommendation framework that ensures user data protection while improving recommendation quality.

June 29, 2026

Local Differential Privacy Integrated Federated Learning Based Large Language Model for Personalized Privacy-Preserving Recommendation System

Key Points

The study aims to develop a privacy-preserving recommendation framework that ensures user data protection while improving recommendation quality.
Integrated local differential privacy with federated learning and transformer models.
Utilized a modified attentional autoregressive XLNet for contextual learning from user interactions.
Implemented recurrent long short coordinate network (RLCN) to generate user preference vectors.
Achieved 99.45% accuracy, 99.45% precision, 99.44% recall, and 99.45% F1-score, indicating high recommendation performance.
Obtained a Hit rate (HR) of 99.2% and NDCG of 98.56 at higher recommendation thresholds.
Demonstrated improved ranking performance with low MAE (0.0055) and RMSE (0.074162).

Abstract

The rapid growth of e-commerce platforms and online user interactions has significantly increased the amount of available user-generated data, which makes recommendation systems essential for delivering personalized content. However, conventional recommendation approaches rely on centralized data, which raises serious concern on user privacy, data leakage and unauthorized access. To address these challenges, this paper proposes a privacy-preserving personalized recommendation framework that integrates local differential privacy (LDP) with federated learning and transformer-based representation learning. In the proposed model, a modified attentional autoregressive XLNet (MA-XLNet) model is employed to learn contextual representation from user reviews and interaction sequences through dual embedding layers and a soft attention mechanism. To capture both short-term and long-term behaviour patterns recurrent long short co-ordinate network (RLCN) is introduced, which integrates LSTM with coordinate attention to generate robust user preference vectors. Furthermore, an LDP based federated learning is incorporated to enable secure distributed model training while preventing the leakage of sensitive user information during parameter aggregation. Experimental evaluations are conducted on the Amazon review dataset containing millions of user reviews and product interactions. The proposed model demonstrates superior recommendation performance compared with baseline models, achieving 99.45% accuracy, 99.45% precision, 99.44% recall and 99.45% F1-score while also achieving improved ranking performance with low MAE (0.0055) and RMSE (0.074162) values. In addition, the model obtains strong ranking metrics with a Hit rate (HR) of 99.2% and a Normalized discounted cumulative Gain (NDCG) value of 98.56 at higher recommendation thresholds indicates improved recommendation quality. The results confirm that integrating transformer based contextual learning with LDP-enabled federated learning effectively enhances both recommendation accuracy and privacy preservation. The proposed framework provides a scalable and secure recommendation architecture suitable for privacy-sensitive environments such as e-commerce and online platforms.

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