A Comprehensive Survey of Incentive Mechanism for Federated Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningSurveySimilar Papers 제목 키워드 기반
A Survey of Federated Evaluation in Federated Learning
In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), whi…
Federated LearningSurveyIntelligent Agents for Auction-based Federated Learning: A Survey
Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., ser…
Federated LearningSurveyIncentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their…
Federated LearningIncentive-Based Federated Learning: Architectural Elements and Future Directions
Federated learning promises to revolutionize machine learning by enabling collaborative model training without compromising data privacy. However, practical adaptability can be limited by critical factors, such as the pa…
Reinforcement LearningFederated LearningTowards Fair Graph Federated Learning via Incentive Mechanisms
Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are s…
FairnessFederated LearningModel Optimization