Open Access

ARTICLE

Trustworthy Personalized Federated Recommender System with Blockchain-Assisted Decentralized Reward Management

Waqar Ali¹, May Altulyan², Ghulam Farooque³, Siyuan Li⁴, Jie Shao^4,5,*

1 Department of Computer Science, College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou, China
2 Department of Computer Engineering, College of Computer Science and Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia
3 Department of Computer Science, The University of Lahore, Lahore, Pakistan
4 Shenzhen Institute of Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
5 Sichuan Artificial Intelligence Research Institute, Yibin, China

* Corresponding Author: Jie Shao. Email:

(This article belongs to the Special Issue: Next-Generation Recommender Systems: Multimodality, Generative Models, and Trustworthy Personalization)

Computers, Materials & Continua 2026, 87(3), 78 https://doi.org/10.32604/cmc.2026.078599

Received 04 January 2026; Accepted 17 February 2026; Issue published 09 April 2026

Abstract

Federated recommender systems (FedRS) enable collaborative model training while preserving user privacy, yet they remain vulnerable to adversarial attacks, unreliable client updates, and misaligned incentives in decentralized environments. Existing approaches struggle to jointly preserve personalization, robustness, and trust when user data are highly non-IID and recommendation quality is governed by ranking-oriented objectives. To address these challenges, we propose a Trustworthy Federated Recommender System (T-FedRS) that extends federated neural collaborative filtering by integrating a ranking-aware reputation mechanism and a lightweight blockchain layer for transparent incentive allocation. Personalization is preserved through locally maintained user embeddings, while item parameters are aggregated using reputation-weighted fusion to suppress malicious updates. Decentralized reputation scores are derived from top-K ranking metrics evaluated on a public probe set and recorded on-chain, enabling verifiable and tamper-resistant reward settlement without exposing raw data or full model updates. Extensive experiments on multiple benchmark datasets demonstrate that T-FedRS consistently outperforms state-of-the-art federated recommender baselines in recommendation accuracy, maintains stable convergence under non-IID data, and remains robust against a high fraction of shilling and model poisoning clients. These findings establish T-FedRS as a practical and effective solution for building trustworthy and personalized FedRS in decentralized settings.

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Keywords

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