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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.
