SoK: Verifiable Integrity Claims for Privacy-Preserving Federated Learning

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Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these are the risks that participating entities may behave dishonestly and fail to perform their tasks correctly. This misbehavior, in turn, also threatens privacy, because an undetected deviation in training or aggregation ca