FedReview: A Review Mechanism for Rejecting Poisoned Updates in
  Federated Learning
Tianhang Zheng Baochun Li
Federated learning has recently emerged as a decentralized approach to learn
a high-performance model without access to user data. Despite its
effectiveness, federated learning gives malicious users opportunities to
manipulate the model by uploading poisoned model updates to the server. In this
paper, we propose a review mechanism called FedReview to identify and decline
the potential poisoned updates in federated learning. Under our mechanism, the
server randomly assigns a subset of clients as reviewers to evaluate the model
updates on their training datasets in each round. The reviewers rank the model
updates based on the evaluation results and count the number of the updates
with relatively low quality as the estimated number of poisoned updates. Based
on review reports, the server employs a majority voting mechanism to integrate
the rankings and remove the potential poisoned updates in the model aggregation
process. Extensive evaluation on multiple datasets demonstrate that FedReview
can assist the server to learn a well-performed global model in an adversarial
environment.