mlsanityData to 14 Oct 2026

Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

arXiv 2411.05874 ↗First posted 7 Nov 2024cs.LG cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

The joint implementation of federated learning (FL) and explainable artificial intelligence (XAI) could allow training models from distributed data and explaining their inner workings while preserving essential aspects of privacy. Toward establishing the benefits and tensions associated with their interplay, this scoping review maps the publications that jointly deal with FL and XAI, focusing on publications that reported an interplay between FL and model interpretability or post-hoc explanations. Out of the 37 studies meeting our criteria, only one explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. The aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. Several studies proposed FL algorithms incorporating explanation methods to safeguard the learning process against defaulting or malicious nodes. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 34 of its 99 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

34 of 99 references.

References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  7. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  8. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  9. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  10. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  11. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  12. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 787
22 more
  1. 2020The Future of Digital Health with Federated Learningcited by 633
  2. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  3. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  4. 2016Not Just a Black Box: Learning Important Features Through Propagating Activation Differencescited by 297
  5. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  6. 2020The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategiescited by 92
  7. 2023Post Hoc Explanations of Language Models Can Improve Language Modelscited by 50
  8. 2019Interpret Federated Learning with Shapley Valuescited by 33
  9. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  10. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  11. 2020Interpretable collaborative data analysis on distributed datacited by 15
  12. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  13. 2023Towards Interpretable Federated Learningcited by 11
  14. 2021Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learningcited by 10
  15. 2022Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Datacited by 9
  16. 2022CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learningcited by 6
  17. 2022Reward Systems for Trustworthy Medical Federated Learningcited by 6
  18. 2023Balancing Privacy Protection and Interpretability in Federated Learningcited by 6
  19. 2021IFedAvg: Interpretable Data-Interoperability for Federated Learningcited by 5
  20. 2022An Interpretable Federated Learning-based Network Intrusion Detection Frameworkcited by 4
  21. 2023FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregationcited by 2
  22. 2022Group Personalized Federated Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025A Survey on Foundation Models for Personalized Federated Intelligencecited by 3
  2. 2025Decoding Federated Learning: The FedNAM+ Conformal Revolutioncited by 1
  3. 2025Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovationcited by 0
  4. 2025Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systemscited by 0
  5. 2026DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphscited by 0
  6. 2026Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learningcited by 0