Towards Understanding Quality Challenges of the Federated Learning: A First Look from the Lens of Robustness
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Federated learning (FL) is a widely adopted distributed learning paradigm in practice, which intends to preserve users' data privacy while leveraging the entire dataset of all participants for training. In FL, multiple models are trained independently on the users and aggregated centrally to update a global model in an iterative process. Although this approach is excellent at preserving privacy by design, FL still tends to suffer from quality issues such as attacks or byzantine faults. Some recent attempts have been made to address such quality challenges on the robust aggregation techniques for FL. However, the effectiveness of state-of-the-art (SOTA) robust FL techniques is still unclear and lacks a comprehensive study. Therefore, to better understand the current quality status and challenges of these SOTA FL techniques in the presence of attacks and faults, in this paper, we perform a large-scale empirical study to investigate the SOTA FL's quality from multiple angles of attacks, simulated faults (via mutation operators), and aggregation (defense) methods. In particular, we perform our study on two generic image datasets and one real-world federated medical image dataset. We also systematically investigate the effect of the distribution of attacks/faults over users and the independent and identically distributed (IID) factors, per dataset, on the robustness results. After a large-scale analysis with 496 configurations, we find that most mutators on each individual user have a negligible effect on the final model. Moreover, choosing the most robust FL aggregator depends on the attacks and datasets. Finally, we illustrate that it is possible to achieve a generic solution that works almost as well or even better than any single aggregator on all attacks and configurations with a simple ensemble model of aggregators.
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