Practical and Bilateral Privacy-preserving Federated Learning
Abstract
Federated learning, as an emerging distributed training model of neural
networks without collecting raw data, has attracted widespread attention.
However, almost all existing researches of federated learning only consider
protecting the privacy of clients, but not preventing model iterates and final
model parameters from leaking to untrusted clients and external attackers. In
this paper, we present the first bilateral privacy-preserving federated
learning scheme, which protects not only the raw training data of clients, but
also model iterates during the training phase as well as final model
parameters. Specifically, we present an efficient privacy-preserving technique
to mask or encrypt the global model, which not only allows clients to train
over the noisy global model, but also ensures only the server can obtain the
exact updated model. Detailed security analysis shows that clients can access
neither model iterates nor the final global model; meanwhile, the server cannot
obtain raw training data of clients from additional information used for
recovering the exact updated model. Finally, extensive experiments demonstrate
the proposed scheme has comparable model accuracy with traditional federated
learning without bringing much extra communication overhead.
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