Distributed Learning with Strategic Users: A Repeated Game Approach
Keywords:Machine Learning (ML), Game Theory And Economic Paradigms (GTEP)
AbstractWe consider a distributed learning setting where strategic users are incentivized by a fusion center, to train a learning model based on local data. The users are not obliged to provide their true gradient updates and the fusion center is not capable of validating the authenticity of reported updates. Thus motivated, we formulate the interactions between the fusion center and the users as repeated games, manifesting an under-explored interplay between machine learning and game theory. We then develop an incentive mechanism for the fusion center based on a joint gradient estimation and user action classification scheme, and study its impact on the convergence performance of distributed learning. Further, we devise adaptive zero-determinant (ZD) strategies, thereby generalizing the classical ZD strategies to the repeated games with time-varying stochastic errors. Theoretical and empirical analysis show that the fusion center can incentivize the strategic users to cooperate and report informative gradient updates, thus ensuring the convergence.
How to Cite
Akbay, A. B., & Zhang, J. (2022). Distributed Learning with Strategic Users: A Repeated Game Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 5976-5983. https://doi.org/10.1609/aaai.v36i6.20543
AAAI Technical Track on Machine Learning I