FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning

Tao Liu; Xuehe Wang

cs.DCApr 12, 2026

FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning

Authors: Tao Liu, Xuehe Wang

Organizations: School of Artificial Intelligence, Sun Yat-sen University, Zhuhai 519082, China

Abstract

Federated learning has become a popular paradigm for privacy protection and edge-based machine learning. However, defending against differential attacks and devising incentive strategies remain significant bottlenecks in this field. Despite recent works on privacy-aware incentive mechanism design for federated learning, few of them consider both data volume and noise level. In this paper, we propose a novel federated learning system called FEDBUD, which combines privacy and economic concerns together by considering the joint influence of data volume and noise level on incentive strategy determination. In this system, the cloud server controls monetary payments to edge nodes, while edge nodes control data volume and noise level that potentially impact the model performance of the cloud server. To determine the mutually optimal strategies for both sides, we model FEDBUD as a two-stage Stackelberg Game and derive the Nash Equilibrium using the mean-field estimator and virtual queue. Experimental results on real-world datasets demonstrate the outstanding performance of FEDBUD.

Figures & tables

Fig. 1 : Illustration of movement trajectory for mean-field estimator $\phi^{t}$ , the cloud server’s strategy $R^{t}$ , edge node $k$ ’ strategy $(B_{k}^{t},\varepsilon_{k}^{t})$ and virtual queues $Q_{k}^{t},Z_{k}^{t}$ over the time horizon.

Panel of Figure 2 — Fig. 2 : Comparison of cloud server’s cost $C$ ( left ) and edge node $k$ ’s utility $U_{k}$ ( right ) over different strategies.

Factor $\gamma_{1}$	Payment $\sum R^{t}$	Model Loss $F(\mathbf{w}^{T})-F(\mathbf{w}^{*})$
$\phantom{0}1\times 10^{-11}$	$398.838\times 10^{6}$	$1.337\times 10^{-3}$
$\phantom{0}5\times 10^{-11}$	$\phantom{0}78.368\times 10^{6}$	$1.347\times 10^{-3}$
$10\times 10^{-11}$	$\phantom{0}38.174\times 10^{6}$	$1.354\times 10^{-3}$

TABLE I : Impact of weight factor

\gamma_{1}

on the cloud server’s objective trade-off.

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May 17, 2025J/K move · Enter open · S save

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

Drashthi Doshi, Avishek Ghosh, Swaprava Nath

IIT Bombay

Incentive Mechanisms for Federated Learning Federated Learning Mechanism Design

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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.

Abstract

Incentive Mechanisms for Federated Learning

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