Search papers, labs, and topics across Lattice.
3
0
4
0
Preserving spatial topology in quantum many-body simulations can drastically reduce compilation overhead, unlocking new efficiencies in quantum computing.
Achieving substantial reductions in training latency, AC$^2$P$^2$SL redefines the efficiency of split learning in edge networks without compromising data privacy.
Achieve Byzantine-robust, privacy-preserving federated learning without sacrificing convergence speed by letting each device autonomously optimize its neighbor selection using a GNN-RL agent.
