Byzantine-Robust Learning on Heterogeneous Datasets via Resampling

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Data Encoding for Byzantine-Resilient Distributed Optimization (2019, background): “, stochastic gradient descent) [10], [21], [23], [25]–[27], [30]–[32] and also with deterministic methods (e.”
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning (2021, background): “We note that, defending the real-world non-iid FL settings from the worst case model poisoning attacks is a well-known challenging task [17], [19], [18] and a limitation of DnC in its current form.”
Learning from History for Byzantine Robust Optimization (2020, background): “Numerous variations have been proposed which can handle non-iid data with varying degrees of success (Li et al., 2019; Ghosh et al., 2019; Chen et al., 2019; Peng et al., 2020; Data & Diggavi, 2020; He et al., 2020a; El-Mhamdi et al., 2020; Dong et al., 2020).”
Federated Learning for Malware Detection in IoT Devices (2021, background): “Algorithm 2 is a slightly adapted version of the second algorithm from [20].”
Ditto: Fair and Robust Federated Learning Through Personalization (2020, background): “In terms of defenses, robust aggregation is a common strategy to mitigate the effect of malicious updates (Blanchard et al., 2017; Pillutla et al., 2019; Sun et al., 2019; Li et al., 2019; He et al., 2020).”