# Towards Interpretable Federated Learning

arXiv: https://arxiv.org/abs/2302.13473 · HTML: https://mlsanity.com/paper/2302.13473/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 67 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 30 of 31)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Not All Samples Are Created Equal: Deep Learning with Importance Sampling](https://mlsanity.com/paper/1803.00942/) (2018)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [On the Accuracy of Influence Functions for Measuring Group Effects](https://mlsanity.com/paper/1905.13289/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning](https://mlsanity.com/paper/2109.02053/) (2021)
- [Active Federated Learning](https://mlsanity.com/paper/1909.12641/) (2019)
- [Game of Gradients: Mitigating Irrelevant Clients in Federated Learning](https://mlsanity.com/paper/2110.12257/) (2021)
- [Interpret Federated Learning with Shapley Values](https://mlsanity.com/paper/1905.04519/) (2019)
- [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019)
- [Fair and efficient contribution valuation for vertical federated learning](https://mlsanity.com/paper/2201.02658/) (2022)
- [Toward Understanding the Influence of Individual Clients in Federated Learning](https://mlsanity.com/paper/2012.10936/) (2020)
- [Transparent Contribution Evaluation for Secure Federated Learning on Blockchain](https://mlsanity.com/paper/2101.10572/) (2021)
- [Interpretable collaborative data analysis on distributed data](https://mlsanity.com/paper/2011.04437/) (2020)
- [FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2302.10417/) (2023)
- [Estimation of Individual Device Contributions for Incentivizing Federated Learning](https://mlsanity.com/paper/2009.09371/) (2020)
- [Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)](https://mlsanity.com/paper/2209.04856/) (2022)
- [Self-explaining deep models with logic rule reasoning](https://mlsanity.com/paper/2210.07024/) (2022)
- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Federated Feature Selection for Cyber-Physical Systems of Systems](https://mlsanity.com/paper/2109.11323/) (2021)
- [GitFL: Adaptive Asynchronous Federated Learning using Version Control](https://mlsanity.com/paper/2211.12049/) (2022)
- [Dynamic Attention-based Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.05765/) (2021)
- [IFedAvg: Interpretable Data-Interoperability for Federated Learning](https://mlsanity.com/paper/2107.06580/) (2021)
- [An Interpretable Federated Learning-based Network Intrusion Detection Framework](https://mlsanity.com/paper/2201.03134/) (2022)

## Built on by (most cited first, 11 of 11)

- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review](https://mlsanity.com/paper/2411.05874/) (2024)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Intelligent Agents for Auction-based Federated Learning: A Survey](https://mlsanity.com/paper/2404.13244/) (2024)
- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance](https://mlsanity.com/paper/2404.12850/) (2024)
- [Decoding Federated Learning: The FedNAM+ Conformal Revolution](https://mlsanity.com/paper/2506.17872/) (2025)
- [Federated Rule Ensemble Method in Medical Data](https://mlsanity.com/paper/2604.17956/) (2026)

## 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.