Convergence analysis for multiple stale-neighbor local steps
Establish a convergence guarantee for the projected-gradient GTVMin algorithm when each communication round performs more than one local update using stale neighbor parameters.
References
For $T_{\mathrm{loc}}>1$, later local steps use stale neighbor parameters; the $T_{\mathrm{loc}}=5$ used in the experiments is a communication-saving heuristic, also open.
Extending them would require replacing di(wt) by the direction that client i actually contributes to the aggregate, namely the average of its τ local LP directionsut i := τ −1 Pτ −1k=0 di(wt i,k), taking ¯ut = Pi ρiut i as the shared direction with effective step αητ (αthe server learning rate of Algorithm 1), and imposing the descent-margin condition on ut i evaluated at the shared point wt. We do not carry this out here, because the LP direction di(·) is a solution map of a linear program whose active set changes with w and is in general not continuous in w; the drift ∥ut i − di(wt)∥ therefore cannot be bounded by O(L0τ ηB) or folded into ∆t i without an additional stability argument for the LP solution, and we make no such claim.