# Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching

arXiv: https://arxiv.org/abs/2405.03782 · HTML: https://mlsanity.com/paper/2405.03782/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [Practical Federated Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04206/) (2019)
- [Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach](https://mlsanity.com/paper/1905.02703/) (2019)
- [Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers](https://mlsanity.com/paper/1909.01463/) (2019)
- [Enhanced Audit Bit Based Distributed Bayesian Detection in the Presence of Strategic Attacks](https://mlsanity.com/paper/2109.13325/) (2021)
- [Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks](https://mlsanity.com/paper/2207.08870/) (2022)
- [Distributed Inference in the Presence of Eavesdroppers: A Survey](https://mlsanity.com/paper/1502.05448/) (2015)
- [Score-Matching Representative Approach for Big Data Analysis with Generalized Linear Models](https://mlsanity.com/paper/1811.00462/) (2018)

## Built on by (most cited first, 4 of 4)

- [TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection](https://mlsanity.com/paper/2501.14302/) (2025)
- [PFedDST: Personalized Federated Learning with Decentralized Selection Training](https://mlsanity.com/paper/2502.07750/) (2025)
- [Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation](https://mlsanity.com/paper/2605.26382/) (2026)
- [E2CB2former: Effecitve and Explainable Transformer for CB2 Receptor Ligand Activity Prediction](https://mlsanity.com/paper/2502.12186/) (2025)

## Abstract (arXiv)

This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions. Additionally, this approach maintains privacy and communication efficiency, and it matches the training performance of models using raw data. Simulation results show that our approach is competitive with or outperforms traditional Federated Learning in accuracy and convergence, especially in scenarios with complex models and a higher number of clients. This framework marks a step forward in integrating human intuition with machine intelligence, which potentially enhances human-machine learning interfaces and collaborative efforts.