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EARS-XTSK: Privacy-Preserving Global Explainability in Cross-Silo Federated Two-Tower Recommendation Systems via Server-Side TSK Fuzzy Rule Distribution

Deniz Altay Avcı1✉
1Adesso Turkey
Received:Sep 10, 2025→Revised:Nov 6, 2025→Accepted:Dec 24, 2025→Published:December 30, 2025
DOI: 10.56038/ejrnd2026526734
Vol. 5, No. 1 · ejrnd2026526734

Abstract

Recommender systems form the both the commercial and the computational backbone of personalization in modern digital ecosystems—from video streaming and e-commerce to news, finance, and social platforms. Yet, the data centralization they require conflicts with privacy regulations such as GDPR and KVKK, which are emphasized more than ever by both the policymakers and the end users, motivating the adoption of privacy-first and transparent solutions. In the privacy context, a prominent approach is Federated Learning (FL). Yet, on its own, it lacks the transparency and interpretability that a centralized solution offers under the same circumstances. We present EARS-XTSK, a privacy-preserving global explainability framework for cross-silo federated recommendation. Our industry setting comprises two real-life data providers in Türkiye: (i) a large online forum representing the user metadata side, and (ii) a commercial streaming platform providing item/content and interaction data. By design, raw user data never leaves partner premises. To enable the global FED-XAI layer without direct data access, we construct a faithful synthetic dataset conforming to the partners’ schema, feature definitions, and scale, and we build an end-to-end clone of their pipelines to de-risk integration. Local sites train Two-Tower retrieval models (user tower on demographics/geo, item tower on multilingual text and metadata) and participate in NVFLARE-based FedAvg aggregation. As our core contribution, we introduce a server-side explainability engine that fuses Grad×Input saliency with a Takagi–Sugeno–Kang (TSK) fuzzy-rule layer to produce interpretable global explanations over item-side labels only (privacy-strict). We provide a complete, reproducible pipeline: dummy data generation (OMDb, SBERT, fastText), Two-Tower embedding/training, federated orchestration (NVFLARE), and Fed-XAI evaluation with visualization. Prototype runs demonstrate faithful global explanations without exposing user identifiers or raw features. In deployment, the same FED-XAI is practically guaranteed to work with the partners’ real local Two-Tower models, since all upstream interfaces are schema-compatible.
Keywords
Reference: (will be filled by editorial office) Keywords: Recommender SystemsFederated LearningTwo-Tower RetrievalExplainable AIFederated ExplainabilityFuzzy Rule-Based SystemsTSK Fuzzy SystemsNVFLARE.Recommender Systems

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References

  1. 1.[1] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282.
  2. 2.[2] K. Bonawitz et al., “Practical secure aggregation for privacy-preserving machine learning,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2017, pp. 1175–1191.
  3. 3.[3] K. Bonawitz et al., “Towards federated learning at scale: System design,” in Proc. Machine Learning and Systems (MLSys), vol. 1, 2019.
  4. 4.[4] P. Kairouz et al., “Advances and open problems in federated learning,” Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021.
  5. 5.[5] M. Ammad-ud-din, E. Ivannikova, S. A. Khan, W. Oyomno, Q. Fu, K. E. Tan, and A. Flanagan, “Federated collaborative filtering for privacy-preserving personalized recommendation system,” arXiv:1901.09888, 2019.
  6. 6.[6] T. Qi, F. Wu, C. Wu, Y. Huang, and X. Xie, “Privacy-preserving news recommendation model learning,” arXiv:2003.09592, 2020.
  7. 7.[7] V. Perifanis and P. S. Efraimidis, “Federated neural collaborative filtering,” Knowledge-Based Systems, vol. 242, Art. no. 108441, 2022.
  8. 8.[8] Z. Sun, Y. Xu, Y. Liu, W. He, L. Kong, F. Wu, Y. Jiang, and L. Cui, “A survey on federated recommendation systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 6–20, 2025.
  9. 9.[9] X. Yi, J. Yang, L. Hong, D. Z. Cheng, L. Heldt, A. Kumthekar, Z. Zhao, L. Wei, and E. H. Chi, “Sampling-bias-corrected neural modeling for large corpus item recommendations,” in Proc. ACM Conf. Recommender Systems (RecSys), 2019, pp. 269–277.
  10. 10.[10] M. Abadi et al., “Deep learning with differential privacy,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2016, pp. 308–318.
  11. 11.[11] S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou, “A hybrid approach to privacy-preserving federated learning,” in Proc. 12th ACM Workshop on Artificial Intelligence and Security (AISec), 2019, pp. 1–11.
  12. 12.[12] B. Hitaj, G. Ateniese, and F. Pérez-Cruz, “Deep models under the GAN: Information leakage from collaborative deep learning,” in Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), 2017, pp. 603–618.
  13. 13.[13] M. Daole, A. Schiavo, J. L. Corcuera Bárcena, P. Ducange, F. Marcelloni, and A. Renda, “OpenFL-XAI: Federated learning of explainable artificial intelligence models in Python,” SoftwareX, vol. 23, Art. no. 101505, 2023.
  14. 14.[14] Y. Zhang and H. Yu, “LR-XFL: Logical reasoning-based explainable federated learning,” arXiv:2308.12681, 2023.
  15. 15.[15] L. M. Lopez-Ramos, F. Leiser, A. Rastogi, S. Hicks, I. Strümke, V. I. Madai, T. Budig, A. Sunyaev, and A. Hilbert, “Interplay between federated learning and explainable artificial intelligence: A scoping review,” arXiv:2411.05874, 2024.
  16. 16.[16] R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, “A survey of methods for explaining black box models,” ACM Computing Surveys, vol. 51, no. 5, Art. no. 93, pp. 1–42, 2018.
  17. 17.[17] M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should I trust you?’ Explaining the predictions of any classifier,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), 2016, pp. 1135–1144.
  18. 18.[18] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
  19. 19.[19] M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in Proc. Int. Conf. Machine Learning (ICML), 2017, pp. 3319–3328.
  20. 20.[20] K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” arXiv:1312.6034, 2013.
  21. 21.[21] M. Ancona, E. Ceolini, C. Öztireli, and M. Gross, “Towards better understanding of gradient-based attribution methods for deep neural networks,” in Proc. Int. Conf. Learning Representations (ICLR), 2018.
  22. 22.[22] L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
  23. 23.[23] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116–132, 1985.
  24. 24.[24] M. Sugeno and G. T. Kang, “Structure identification of fuzzy model,” Fuzzy Sets and Systems, vol. 28, no. 1, pp. 15–33, 1988.
  25. 25.[25] L.-X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, no. 6, pp. 1414–1427, 1992.
  26. 26.[26] F. Aghaeipoor, M. Sabokrou, and A. Fernández, “Fuzzy rule-based explainer systems for deep neural networks: From local explainability to global understanding,” IEEE Transactions on Fuzzy Systems, vol. 31, no. 9, pp. 3069–3080, 2023.
  27. 27.[27] M. T. Ribeiro, S. Singh, and C. Guestrin, “Anchors: High-precision model-agnostic explanations,” in Proc. AAAI Conf. Artificial Intelligence, vol. 32, no. 1, 2018, pp. 1527–1535.
  28. 28.[28] Y. Lou, R. Caruana, and J. Gehrke, “Intelligible models for classification and regression,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), 2012, pp. 150–158.
  29. 29.[29] R. Caruana et al., “Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), 2015, pp. 1721–1730.
  30. 30.[30] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proc. Int. Conf. World Wide Web (WWW), 2017, pp. 173–182.
  31. 31.[31] X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T.-S. Chua, “Explainable reasoning over knowledge graphs for recommendation,” in Proc. AAAI Conf. Artificial Intelligence, vol. 33, no. 1, 2019, pp. 5329–5336.
  32. 32.[32] Y. Xian, Z. Fu, S. Muthukrishnan, G. de Melo, and Y. Zhang, “Reinforcement knowledge graph reasoning for explainable recommendation,” in Proc. Int. ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2019, pp. 285–294.
  33. 33.[33] X. Chen, Y. Zhang, H. Xu, Y. Cao, Z. Qin, and H. Zha, “Visually explainable recommendation,” arXiv:1801.10288, 2018.
  34. 34.[34] A. Gonçalves, P. Ray, B. Soper, J. Stevens, L. Coyle, and A. P. Sales, “Generation and evaluation of synthetic patient data,” BMC Medical Research Methodology, vol. 20, Art. no. 108, 2020.
  35. 35.[35] B. van Breugel, Z. Qian, and M. van der Schaar, “Synthetic data, real errors: How (not) to publish and use synthetic data,” in Proc. Int. Conf. Machine Learning (ICML), 2023, pp. 34793–34808.
  36. 36.[36] H. R. Roth et al., “NVIDIA FLARE: Federated learning from simulation to real-world,” arXiv:2210.13291, 2022.
  37. 37.[37] NVIDIA, “NVIDIA FLARE documentation,” NVIDIA FLARE 2.7.0 documentation. [Online]. Available: https://nvflare.readthedocs.io/en/main/. Accessed: May 11, 2026.

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Cite This Article
Avcı, D. A. (2025). EARS-XTSK: Privacy-Preserving Global Explainability in Cross-Silo Federated Two-Tower Recommendation Systems via Server-Side TSK Fuzzy Rule Distribution. *The European Journal of Research and Development*, 5(1), ejrnd2026526734. https://doi.org/10.56038/ejrnd2026526734

Bibliographic Info

JournalThe European Journal of Research and Development
Volume5
Issue1
Pages1–19
Article IDejrnd2026526734
PublishedDecember 30, 2025
eISSN2822-2296

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