However, these parameters are often high-dimensional and hard to interpret.

However, training EBMs on data in discrete or mixed state spaces poses significant challenges due to the lack of robust and fast sampling methods. In this work, we propose to train discrete EBMs with Energy Discrepancy, a loss function which only requires the evaluation of the energy function at data points and their perturbed counterparts, thus eliminating the need for Markov chain Monte Carlo.

Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.