Fair swarm learning: Improving incentives for collaboration by a fair reward mechanism
Mohammad Tajabadi, Dominik Heider
Abstract
Swarm learning is an emerging technique for collaborative machine learning in which several participants train machine learning models without sharing private data. In a standard swarm network, all the nodes in the network receive identical final models regardless of their individual contributions. This mechanism may be deemed unfair from an economic perspective, discouraging organizations with more resources from participating in any collaboration. Here, we present a framework for swarm learning in which nodes receive personalized models based on their contributions. The results of this study demonstrate the efficacy of this approach by showing that all participants experience performance enhancements compared to their local models. However, participants with higher contributions receive better models than those with lower contributions. This fair mechanism results in the highest possible accuracy for the most contributive participant, comparable to the standard swarm learning model. Such incentive structure can motivate resource-rich organizations to engage in collaboration, leading to the development of machine learning models that incorporate data from more resources, which is ultimately beneficial for every party.