@article{Donahue_Kleinberg_2021, title={Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/16669}, DOI={10.1609/aaai.v35i6.16669}, abstractNote={Federated learning is a setting where agents, each with access to their own data source, combine models learned from local data to create a global model. If agents are drawing their data from different distributions, though, federated learning might produce a biased global model that is not optimal for each agent. This means that agents face a fundamental question: should they join the global model or stay with their local model? In this work, we show how this situation can be naturally analyzed through the framework of coalitional game theory. Motivated by these considerations, we propose the following game: there are heterogeneous players with different model parameters governing their data distribution and different amounts of data they have noisily drawn from their own distribution. Each player’s goal is to obtain a model with minimal expected mean squared error (MSE) on their own distribution. They have a choice of fitting a model based solely on their own data, or combining their learned parameters with those of some subset of the other players. Combining models reduces the variance component of their error through access to more data, but increases the bias because of the heterogeneity of distributions. In this work, we derive exact expected MSE values for problems in linear regression and mean estimation. We use these values to analyze the resulting game in the framework of hedonic game theory; we study how players might divide into coalitions, where each set of players within a coalition jointly constructs a single model. In a case with arbitrarily many players that each have either a "small" or "large" amount of data, we constructively show that there always exists a stable partition of players into coalitions. }, number={6}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Donahue, Kate and Kleinberg, Jon}, year={2021}, month={May}, pages={5303-5311} }