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Multimodal unlearning could revolutionize how we handle sensitive data in AI, enabling targeted removal without sacrificing model performance.
Federated recommendation systems can learn more robust item embeddings, and thus perform better, by using sharpness-aware minimization to combat data heterogeneity and sparsity.
