# Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training

arXiv: https://arxiv.org/abs/2305.02942 · HTML: https://mlsanity.com/paper/2305.02942/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks](https://mlsanity.com/paper/1911.07135/) (2019)
- [Unlocking High-Accuracy Differentially Private Image Classification through Scale](https://mlsanity.com/paper/2204.13650/) (2022)
- [Understanding Gradient Clipping in Private SGD: A Geometric Perspective](https://mlsanity.com/paper/2006.15429/) (2020)
- [Deep Learning Through the Lens of Example Difficulty](https://mlsanity.com/paper/2106.09647/) (2021)
- [Estimating Example Difficulty Using Variance of Gradients](https://mlsanity.com/paper/2008.11600/) (2020)
- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Collaborative Machine Learning with Incentive-Aware Model Rewards](https://mlsanity.com/paper/2010.12797/) (2020)
- [Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy](https://mlsanity.com/paper/2009.06389/) (2020)
- [Toward Understanding the Influence of Individual Clients in Federated Learning](https://mlsanity.com/paper/2012.10936/) (2020)
- [Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks](https://mlsanity.com/paper/2203.00481/) (2022)
- [How Do Input Attributes Impact the Privacy Loss in Differential Privacy?](https://mlsanity.com/paper/2211.10173/) (2022)
- [Distributed Machine Learning and the Semblance of Trust](https://mlsanity.com/paper/2112.11040/) (2021)

## Built on by (most cited first, 3 of 3)

- [SoK: Memorisation in machine learning](https://mlsanity.com/paper/2311.03075/) (2023)
- [Data value estimation on private gradients](https://mlsanity.com/paper/2412.17008/) (2024)
- [Privacy-Preserving Dataset Combination](https://mlsanity.com/paper/2502.05765/) (2025)

## Abstract (arXiv)

Obtaining high-quality data for collaborative training of machine learning models can be a challenging task due to A) regulatory concerns and B) a lack of data owner incentives to participate. The first issue can be addressed through the combination of distributed machine learning techniques (e.g. federated learning) and privacy enhancing technologies (PET), such as the differentially private (DP) model training. The second challenge can be addressed by rewarding the participants for giving access to data which is beneficial to the training model, which is of particular importance in federated settings, where the data is unevenly distributed. However, DP noise can adversely affect the underrepresented and the atypical (yet often informative) data samples, making it difficult to assess their usefulness. In this work, we investigate how to leverage gradient information to permit the participants of private training settings to select the data most beneficial for the jointly trained model. We assess two such methods, namely variance of gradients (VoG) and the privacy loss-input susceptibility score (PLIS). We show that these techniques can provide the federated clients with tools for principled data selection even in stricter privacy settings.