  Owing to the low communication costs and privacy-promoting capabilities,
Federated Learning (FL) has become a promising tool for training effective
machine learning models among distributed clients. However, with the
distributed architecture, low quality models could be uploaded to the
aggregator server by unreliable clients, leading to a degradation or even a
collapse of training. In this paper, we model these unreliable behaviors of
clients and propose a defensive mechanism to mitigate such a security risk.
Specifically, we first investigate the impact on the models caused by
unreliable clients by deriving a convergence upper bound on the loss function
based on the gradient descent updates. Our theoretical bounds reveal that with
a fixed amount of total computational resources, there exists an optimal number
of local training iterations in terms of convergence performance. We further
design a novel defensive mechanism, named deep neural network based secure
aggregation (DeepSA). Our experimental results validate our theoretical
analysis. In addition, the effectiveness of DeepSA is verified by comparing
with other state-of-the-art defensive mechanisms.
