Cross-silo federated learning (CFL) enables organizations (e.g., hospitals or banks) to collaboratively train artificial intelligence (AI) models while preserving data privacy by keeping data local. While prior work has primarily addressed statistical heterogeneity across organizations, a critical challenge arises from economic competition, where organizations may act as market rivals, making them hesitant to participate in joint training due to potential utility loss (i.e., reduced net benefit). Furthermore, the combined effects of statistical heterogeneity and inter-organizational competition on organizational behavior and system-wide social welfare remain underexplored. In this paper, we propose CoCoGen, a coopetitive-compatible data generation framework, leveraging generative AI (GenAI) and potential game theory to model, analyze, and optimize collaborative learning under heterogeneous and competitive settings. Specifically, CoCoGen characterizes competition and statistical heterogeneity through learning performance and utility-based formulations and models each training round as a weighted potential game. We then derive GenAI-based data generation strategies that maximize social welfare. Experimental results on the Fashion-MNIST dataset reveal how varying heterogeneity and competition levels affect organizational behavior and demonstrate that CoCoGen consistently outperforms baseline methods.

Early adopters of AI will share a global profit pool valued at $1 trillion. Will your company be among them? From the dexterity of Amazon's Kiva robots to the facial recognition in Apple's iPhone X, artificial intelligence is increasingly sophisticated and accessible. It also promises to be a rich source of profit uplift -- up to 10% of revenue, depending on your industry. Nevertheless, more than 95% of companies have not embraced AI technology to reinvent how they do business.1 Even though there are many unknowns regarding AI's capabilities and uses, our research at the McKinsey Global Institute suggests that following a wait-and-see strategy for too much longer could be a costly mistake.

Early adopters of AI will share a global profit pool valued at $1 trillion. Will your company be among them? From the dexterity of Amazon's Kiva robots to the facial recognition in Apple's iPhone X, artificial intelligence is increasingly sophisticated and accessible. It also promises to be a rich source of profit uplift -- up to 10% of revenue, depending on your industry. Nevertheless, more than 95% of companies have not embraced AI technology to reinvent how they do business.1