A Comprehensive Survey of Incentive Mechanism for Federated Learning

TL;DR

This paper surveys incentive mechanisms in federated learning, highlighting how to motivate resource contribution through various economic and computational techniques to improve performance and participation.

Contribution

It provides a comprehensive taxonomy and comparison of existing incentive schemes, and identifies future research directions in federated learning incentives.

Findings

Summarizes incentive techniques like Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain.

Analyzes the strengths and limitations of current incentive mechanisms.

Proposes future research directions for incentive design in federated learning.

Abstract

Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.