A Comprehensive Survey of Incentive Mechanism for Federated Learning
This is an incremental survey for researchers in federated learning, summarizing existing methods to improve participation.
The paper surveys incentive mechanisms for federated learning, addressing the problem of insufficient participant resources by reviewing techniques like Stackelberg games and auctions, but does not present new experimental results or concrete numbers.
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.