mlsanityData to 7 Oct 2026

Towards Interpretable Federated Learning

arXiv 2302.13473 ↗First posted 27 Feb 2023cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. Thus, interpretable federated learning (IFL) has become an emerging topic of research attracting significant interest from the academia and the industry alike. Its interdisciplinary nature can be challenging for new researchers to pick up. In this paper, we bridge this gap by providing (to the best of our knowledge) the first survey on IFL. We propose a unique IFL taxonomy which covers relevant works enabling FL models to explain the prediction results, support model debugging, and provide insights into the contributions made by individual data owners or data samples, which in turn, is crucial for allocating rewards fairly to motivate active and reliable participation in FL. We conduct comprehensive analysis of the representative IFL approaches, the commonly adopted performance evaluation metrics, and promising directions towards building versatile IFL techniques.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 31 of its 67 references that are in the map. First posted on arXiv 27 Feb 2023.

Builds on

31 of 67 references.

References · 31

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  5. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  6. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 323
  7. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  8. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  9. 2019On the Accuracy of Influence Functions for Measuring Group Effectscited by 146
  10. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  11. 2019Measure Contribution of Participants in Federated Learningcited by 78
  12. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
19 more
  1. 2019Active Federated Learningcited by 63
  2. 2021Game of Gradients: Mitigating Irrelevant Clients in Federated Learningcited by 35
  3. 2019Interpret Federated Learning with Shapley Valuescited by 33
  4. 2019A Crowdsourcing Framework for On-Device Federated Learningcited by 32
  5. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  6. 2020Toward Understanding the Influence of Individual Clients in Federated Learningcited by 16
  7. 2021Transparent Contribution Evaluation for Secure Federated Learning on Blockchaincited by 16
  8. 2020Interpretable collaborative data analysis on distributed datacited by 15
  9. 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
  10. 2020Estimation of Individual Device Contributions for Incentivizing Federated Learningcited by 14
  11. 2022Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)cited by 13
  12. 2022Self-explaining deep models with logic rule reasoningcited by 13
  13. 2021Privacy-Preserving Feature Selection with Secure Multiparty Computationcited by 12
  14. 2021Federated Feature Selection for Cyber-Physical Systems of Systemscited by 11
  15. 2022GitFL: Adaptive Asynchronous Federated Learning using Version Controlcited by 10
  16. 2021Dynamic Attention-based Communication-Efficient Federated Learningcited by 9
  17. 2021IFedAvg: Interpretable Data-Interoperability for Federated Learningcited by 5
  18. 2022An Interpretable Federated Learning-based Network Intrusion Detection Frameworkcited by 4
  19. 2023Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropoutcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 1; 2026: 1.

11 papers.

  1. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  2. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  3. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  4. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  5. 2024Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Reviewcited by 6
  6. 2023Trustworthy Federated Learning: A Surveycited by 4
  7. 2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
  8. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  9. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  10. 2025Decoding Federated Learning: The FedNAM+ Conformal Revolutioncited by 1
  11. 2026Federated Rule Ensemble Method in Medical Datacited by 0