One-shot Robust Federated Learning of Independent Component Analysis

Excerpt

arXiv:2505.20532v2 Announce Type: replace Abstract: This paper studies robust one-shot aggregation for distributed and federated Independent Component Analysis (ICA). In this setting, each client computes a local ICA estimator, while the server aims to recover a common global mixing matrix without accessing raw data. The main difficulty is that local ICA estimators are identifiable only up to signed permutations and may have highly heterogeneous estimation quality. We propose Spectral-Robust-Fed