SC-DN establishes a global first-order stationary point per round and solves a mixed-integer signomial program to optimize four control variables for VFL, yielding better classification performance and lower resource use than greedy baselines on image and multi-modal data.
CVXPY: A Python-embedded modeling lan- guage for convex optimization
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LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
SBAMP combines RRT* global planning with an online Lyapunov-stable SEDS-inspired controller requiring no pre-trained data to enable real-time adaptation while keeping global path structure.
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Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
SC-DN establishes a global first-order stationary point per round and solves a mixed-integer signomial program to optimize four control variables for VFL, yielding better classification performance and lower resource use than greedy baselines on image and multi-modal data.
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Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
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SBAMP: Sampling Based Adaptive Motion Planning
SBAMP combines RRT* global planning with an online Lyapunov-stable SEDS-inspired controller requiring no pre-trained data to enable real-time adaptation while keeping global path structure.
- Minimizing the Expected Cost of Synchronization in Lossless Power Networks