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However, FL remains vulnerable to adversarial attacks such as model poisoning, data injection, and model inversion that compromise model integrity. To address these challenges, this paper presents a blockchain-based personalized federated learning (FL) framework designed to enhance the security, privacy, and efficiency of decentralized model training in healthcare environments. It integrates Practical Byzantine Fault Tolerance (PBFT) for tamper-resistant aggregation, L2-norm anomaly filtering for lightweight adversarial defense, and a Neural Architecture Search (NAS)-optimized hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention to enable efficient, personalized modeling of non-IID healthcare data. Together, these components address key FL challenges, including robustness to model poisoning, accuracy, and deployment on resource-constrained devices. To evaluate its effectiveness, we apply the proposed framework to drug recommendation tasks using three real-world medical datasets, namely Symptom2Disease, UCL Drug, and Dermo Questions, achieving F1-scores of 0.97, 0.70, and 0.83, respectively. The framework demonstrates competitive performance compared to conventional and state-of-the-art methods while significantly reducing the number of trainable parameters, highlighting its suitability for real-time, on-device healthcare applications. These results validate the framework\u2019s ability to deliver secure, personalized, and privacy-preserving recommendations in intelligent healthcare systems.<\/jats:p>","DOI":"10.1007\/s00521-025-11828-9","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T02:18:43Z","timestamp":1768789123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Blockchain-based personalized federated learning framework for drug recommendation systems resilient to model poisoning"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7923-5253","authenticated-orcid":false,"given":"Sina","family":"Apak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ismail Tuncer","family":"Degim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samaneh","family":"Zahertar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"11828_CR1","doi-asserted-by":"publisher","unstructured":"Chen S, Xue D, Chuai G, Yang Q, Liu Q (2020) FL-QSAR: A federated learning-based QSAR prototype for collaborative drug discovery. 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