Incentivizing Data Contributions in Decentralized Collaborative Learning

In a collaborative learning scheme such as the federated learning model, each user benefits from the data contribution of others. Previous work shows that the federated learning protocol can incentivize users to contribute more than in the competitive equilibrium by penalizing deviations. However, a central controller with access to all the data may raise privacy concerns. In this work, we construct a decentralized collaborative protocol in which users share data without relying on a centralized controller. We then extend this protocol to a repeated game and analyze the competitive equilibrium behavior, along with strategies users can implement to foster collaboration in the repeated setting of the protocol. We provide a quantitative analysis of free-rider behavior under decentralized protocols and compare the amount of information collected with decentralized protocols against that in the centralized protocol.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Wang, Yuxiao

Advisor dc:contributor.advisor

• Bates, Stephen

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162977

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162977