Aggregators¶
Byzantine-resilient gradient aggregation rules.
In distributed learning with \(n\) workers of which up to \(f\) may be Byzantine (adversarial or faulty), the aggregator is the single point that determines whether the training converges. Each rule in this module accepts one gradient per worker and produces a single aggregated gradient that is robust to the \(f\) worst outliers.
All aggregators are stateless: each rule is a @classmethod invoked
directly on the class. Specialized parameters (\(f\), \(n\), \(m\)) are
keyword-only.