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Blockchain-enabled Trustworthy Federated Unlearning
However, existing works require central servers to retain the historical model parameters from distributed clients, such that allows the central server to utilize these parameters for further training even, after the clients exit the training process.
Scalable Federated Unlearning via Isolated and Coded Sharding
Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models.
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