robust aggregation

**Robust Aggregation** in federated learning is the **use of Byzantine-resilient aggregation rules to combine client updates** — replacing simple averaging (which is vulnerable to a single malicious client) with robust statistics that tolerate a fraction of corrupted or adversarial updates. **Robust Aggregation Methods** - **Coordinate-Wise Median**: Take the median of each gradient coordinate across clients. - **Trimmed Mean**: Remove the highest and lowest values for each coordinate, then average. - **Krum/Multi-Krum**: Select the update(s) closest to the majority of other updates. - **Bulyan**: Combine Krum selection with trimmed mean for stronger robustness. **Why It Matters** - **Byzantine Resilience**: Tolerates up to $f < n/2$ malicious or faulty clients (depending on the method). - **Poisoning Defense**: Robust aggregation is the primary defense against federated learning poisoning attacks. - **No Accuracy Loss**: With few Byzantine clients, robust aggregation matches FedAvg performance. **Robust Aggregation** is **majority rules, outliers rejected** — using robust statistics to aggregate client updates while ignoring adversarial or corrupt contributions.