Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

ArXi:2505.12010v4 Announce Type: replace-cross Classical federated learning (FL) assumes that the clients have a limited amount of noisy data with which they voluntarily participate and contribute towards learning a global, accurate model in a principled manner. The learning happens in a distributed fashion without sharing the data with the center. However, these methods do not consider the incentive of an agent for participating and contributing to the process, given that data collection and running a distributed algorithm is costly for the clients.