New analysis framework yields tighter linear convergence for FedExProx on non-strongly convex quadratics and PL functions, proving outperformance over GD once communication costs are counted.
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APAPC integrates Nesterov acceleration into primal-dual forward-backward schemes by exploiting dual strong convexity to achieve optimal sublinear and accelerated linear convergence rates.
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Tighter Performance Theory of FedExProx
New analysis framework yields tighter linear convergence for FedExProx on non-strongly convex quadratics and PL functions, proving outperformance over GD once communication costs are counted.
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A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization
APAPC integrates Nesterov acceleration into primal-dual forward-backward schemes by exploiting dual strong convexity to achieve optimal sublinear and accelerated linear convergence rates.