Byzantine-Robust NonIID optimizer

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

An implementation of a bucketing-based Byzantine-robust aggregation strategy for federated learning with heterogeneous (non-IID) data distributions. The bucketing technique reduces the effective variance introduced by data heterogeneity, enabling standard robust aggregation rules to function correctly in the non-IID setting. Evaluated against state-of-the-art attacks and baselines in PyTorch.