Incentive Mechanism Design for Federated Learning: Challenges and Opportunities

Federated learning is a new distributed machine learning paradigm that many clients (e.g., mobile devices or organizations) collaboratively train a model under the orchestration of a parameter server (e.g., service provider), while keeping the training data locally. One of the main challenges in federated learning is the data island, that is, each client maintains its local data and has no incentive for contributing data to model training if no reward is granted. Thus, we must motivate a large number of clients to participate in federated learning to break the limitation of data in the form of isolated islands. We discuss the fundamental research challenges in the incentive mechanism design for federated learning, and present a general framework with potential solutions to the challenges. Experiments are conducted to verify the effectiveness of the proposed framework. With several future research directions identified in incentive mechanism design for federated learning, we expect that more research interest will be stimulated in this novel area.