AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We introduce a tensor envelope (TEE) for composite objectives $\varphi=f+g$, built from a $q$th-order Taylor model of the smooth term $f$ regularized by a $p$th power with $p>q$. To construct it, we first establish a tensor descent lemma and characterize its behavior in the three regimes $q<p<q+1$, $p=q+1$, and $p>q+1$...
HiFedProx is introduced, which replaces the quadratic penalty with a scale-matched power-type regularizer indexed by p, and combines this geometry with finite-budget stochastic client optimization and same-minibatch Armijo backtracking.
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