Federated learning allows multiple participants to collaboratively train an
efficient model without exposing data privacy. However, this distributed
machine learning training method is prone to attacks from Byzantine clients,
which interfere with the training of the global model by modifying the model or
uploading the false gradient. In this paper, we propose a novel serverless
federated learning framework Committee Mechanism based Federated Learning
(CMFL), which can ensure the robustness of the algorithm with convergence
guarantee. In CMFL, a committee system is set up to screen the uploaded local
gradients. The committee system selects the local gradients rated by the
elected members for the aggregation procedure through the selection strategy,
and replaces the committee member through the election strategy. Based on the
different considerations of model performance and defense, two opposite
selection strategies are designed for the sake of both accuracy and robustness.
Extensive experiments illustrate that CMFL achieves faster convergence and
better accuracy than the typical Federated Learning, in the meanwhile obtaining
better robustness than the traditional Byzantine-tolerant algorithms, in the
manner of a decentralized approach. In addition, we theoretically analyze and
prove the convergence of CMFL under different election and selection
strategies, which coincides with the experimental results.

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