Jiashuo Li

Byzantine-robust federated learning can be sped up by orders of magnitude with provable convergence guarantees and minimal impact on error floors, simply by projecting gradients into a drastically smaller subspace before aggregation.

Achieve better accuracy in federated learning with imbalanced data and low communication costs by mimicking the brain's efficient knowledge integration.