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Spectral analysis of client feature representations can identify and relabel noisy data in federated learning, outperforming existing noise-tolerant loss and loss-dynamic approaches.
By clustering users based on the geometry of their feature spaces *before* training, FB-NLL sidesteps the vulnerability of existing federated learning methods to noisy labels and corrupted updates.
