Federated learning screens clients before round one: one wavelet summary each, training survives 90% malicious
Federated learning can screen out poisoned clients before the first round: each client sends a single wavelet scattering summary, and a denoising autoencoder trained only on clean data scores them, keeping training alive when 90% of clients are malicious.