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A learning federation is composed of multiple participants who use thefederated learning technique to collaboratively train a machine learning modelwithout directly revealing the local data. Nevertheless, the existing federatedlearning frameworks have a serious defect that even a participant is revoked,its data are still remembered by the trained model. In a company-levelcooperation, allowing the remaining companies to use a trained model thatcontains the memories from a revoked company is obviously unacceptable, becauseit can lead to a big conflict of interest. Therefore, we emphatically discussthe participant revocation problem of federated learning and design a revocablefederated random forest (RF) framework, RevFRF, to further illustrate theconcept of revocable federated learning. In RevFRF, we first define thesecurity problems to be resolved by a revocable federated RF. Then, a suite ofhomomorphic encryption based secure protocols are designed for federated RFconstruction, prediction and revocation. Through theoretical analysis andexperiments, we show that the protocols can securely and efficiently implementcollaborative training of an RF and ensure that the memories of a revokedparticipant in the trained RF are securely removed.

Revocable Federated Learning: A Benchmark of Federated Forest