Title: Fisher Information for Robust Federated Cross-Validation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: covariate shift, federated learning, Fisher information, KL divergence

Abstract: When training data are fragmented across batches or federated-learned across different geographic locations, trained models manifest performance degradation. That degradation partly  owes to  covariate shift induced by data having been fragmented across time and space and producing  dissimilar empirical training distributions. Each fragment's distribution is slightly different to a hypothetical unfragmented training distribution of covariates, and to the single validation distribution.  To address this problem, we propose Fisher Information for Robust fEderated validation (\textbf{FIRE}). This  method  accumulates  fragmentation-induced covariate shift divergences from the global training distribution  via an approximate Fisher information. That term, which we prove to be a more computationally-tractable estimate, is then used as a per-fragment loss penalty, enabling scalable distribution alignment.  FIRE outperforms importance weighting benchmarks by $5.1\%$ at maximum and federated learning (FL) benchmarks by up to $5.3\%$ on shifted validation sets.