Abstract:We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a federated learning setting by relying on multiparty homomorphic encryption (MHE). RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates the federated learning attacks that target the gradients under a passive-adversary threat model. We propose a novel packing scheme, multi-dimensional packing, for a better utilization of Si… Show more
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