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As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall framework when learning over a sequence of tasks results in the phenomenon known as ``catastrophic forgetting''.
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Reducing forgetting in federated learning with truncated cross-entropy
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Revfrf: Enabling cross-domain random forest training with revocable federated learning
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Federated continual learning through distillation in pervasive computing
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Federated unlearning via class-discriminative pruning
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Federated unlearning with knowledge distillation
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A survey on gradient inversion: Attacks, defenses and future directions
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Fedrecover: Recovering from poisoning attacks in federated learning using historical information
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