Giskard : Byzantine Robust and Private Aggregation for Large-Scale Decentralized Learning

Giskard is a scalable protocol for private and Byzantine‑robust decentralized aggregation in large‑scale learning. It organizes participants into a tree of committees of size O(log n) and computes a differentially private coordinate‑wise approximate median using secure multi‑party computation within each committee. Experiments with up to one million participants show that Giskard reduces per‑party communication by nearly three orders of magnitude compared to its closest competitors while maintaining robustness against up to n/4 Byzantine parties.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• safety