A server that keeps one example per class can detect and drop malicious federated-learning clients by measuring how separated their learned embeddings are, via the norm of a Gram matrix.
A little is enough: Circumventing defenses for distributed learning
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FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix
A server that keeps one example per class can detect and drop malicious federated-learning clients by measuring how separated their learned embeddings are, via the norm of a Gram matrix.