# Motivating Workers in Federated Learning: A Stackelberg Game Perspective

## TL;DR

This paper proposes an incentive mechanism modeled as a Stackelberg game to motivate heterogeneous workers in federated learning, improving convergence by balancing worker diversity and training latency.

## Contribution

It introduces an analytical equilibrium solution for incentivizing workers in federated learning using a Stackelberg game framework.

## Key findings

- Optimal number of workers depends on budget and trade-offs.
- Limited budget requires careful worker selection to balance diversity and latency.
- Analytical solution guides incentive design for federated learning.

## Abstract

Due to the large size of the training data, distributed learning approaches such as federated learning have gained attention recently. However, the convergence rate of distributed learning suffers from heterogeneous worker performance. In this paper, we consider an incentive mechanism for workers to mitigate the delays in completion of each batch. We analytically obtained equilibrium solution of a Stackelberg game. Our numerical results indicate that with a limited budget, the model owner should judiciously decide on the number of workers due to trade off between the diversity provided by the number of workers and the latency of completing the training.