To do so, we first form a family of unnormalized densities (or a "path") parameterized by 2 [0, 1] between the two distributions of interest

Hedonic Games (HGs) are a classical framework modeling coalition formation of strategic agents guided by their individual preferences.

In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation.

Our proposed method significantly reduces communication overhead in federated learning. This method poses a trade-off between time and memory complexity. We also provide detailed information about the optimization hyperparameters e.g. In this section, we explore the effect of fitness sparsification i.e. selecting top-k fitness values from the To enable a fair and insightful comparison between the two population sizes, our focus was on assessing performance based on the number of members remaining post-sparsification rather than directly contrasting sparsification rates. Our results underline the crucial role that population size plays in exploring optimal solutions, overshadowing even the significance of compression rate.