An Encouraging Design for Data Owners to Join Multiple Co-existing Federated Learning

Abstract

Federated learning is a distributed learning system that addresses the distributed difficulty such as communication overhead and private information in machine learning while maintaining high performance. However, the distributed learners have to dedicate their resources to improving the global model, which is not likely to happen voluntarily. This motivated us to design an incentive mechanism for users (data owners) to actively participate in the FL processes. In this paper, we consider multiple co-existing FL service providers (FLSPs) with the need to train their models and multiple data owners (DOs) that can offer that service. In the system, DO, and FLSP will submit their cost and valuation values to the cloud platform. Based on this information, we formulate an optimization problem that aims to maximize the social welfare under the nonnegative utility constraint and maximum gain of FLSPs. Then, we propose a heuristic algorithm, Binary Whale Optimization Algorithm (B-WOA), that can solve our formulated NP-hard problem in polynomial time. Finally, numerical results are shown to demonstrate the effectiveness of our proposed algorithm. Moreover, we also compare the performance of our proposed algorithm with Hungarian and greedy algorithms.