# Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

arXiv: https://arxiv.org/abs/2505.12010 · HTML: https://mlsanity.com/paper/2505.12010/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [Error Feedback Fixes SignSGD and other Gradient Compression Schemes](https://mlsanity.com/paper/1901.09847/) (2019)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation](https://mlsanity.com/paper/2010.00753/) (2020)
- [FedCoin: A Peer-to-Peer Payment System for Federated Learning](https://mlsanity.com/paper/2002.11711/) (2020)
- [One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning](https://mlsanity.com/paper/2103.03228/) (2021)
- [Mechanisms that Incentivize Data Sharing in Federated Learning](https://mlsanity.com/paper/2207.04557/) (2022)
- [Fairness in Federated Learning via Core-Stability](https://mlsanity.com/paper/2211.02091/) (2022)
- [Redefining Contributions: Shapley-Driven Federated Learning](https://mlsanity.com/paper/2406.00569/) (2024)
- [Communication-Efficient and Byzantine-Robust Distributed Learning with Error Feedback](https://mlsanity.com/paper/1911.09721/) (2019)
- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning](https://mlsanity.com/paper/2305.05221/) (2023)
- [FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?](https://mlsanity.com/paper/2405.13879/) (2024)
- [Incentivizing Truthful Collaboration in Heterogeneous Federated Learning](https://mlsanity.com/paper/2412.00980/) (2024)

## Built on by (most cited first, 1 of 1)

- [Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation](https://mlsanity.com/paper/2603.20873/) (2026)

## Abstract (arXiv)

In federated learning (FL), a center trains a shared model from updates that clients compute on their private data. Collecting and training on data is costly, so a strategic client may contribute little data and free-ride on the model trained by the others. We study mechanisms that incentivize data contribution and learn the model at the same time. Our first mechanism, UPBReD, updates the clients ' contributions and the model jointly, and converges at a geometric rate to a Nash equilibrium of the contribution game together with the welfare-maximizing model for those contributions. This equilibrium can involve under-contribution, so our second mechanism, 2P-UPBReD, adds budget-balanced transfers that make full contribution a dominant strategy, after which training converges to the optimal model; we also give conditions under which every client prefers to participate. Both mechanisms trust what clients report about the data they use and the gradients they compute. We show that no payment rule can reward unverifiable contributions unless something the center observes depends on the data actually used. Building on this, T-2P-UPBReD checks each client's reported contribution against the other clients ' realized valuations of the trained model, and prices gradient reports with a budget-balanced charge. It elicits truthful contribution reports, makes gradient reports approximately truthful, still reaches full contribution and the optimal model, and never runs a deficit. Experiments on CIFAR-10, FEMNIST, and Twitter show that 2P-UPBReD matches the welfare of FedAvg trained on all data, while strategic clients without incentives lower the welfare of the learned model.