A cost-preserving transformation enforces information-theoretic secrecy in distributed computing via null-space augmentation of the allocation matrix and shared randomness injection.
Communication-computation efficient gradient coding,
2 Pith papers cite this work. Polarity classification is still indexing.
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The GMUDC framework provides topology-aware upper and lower bounds on reconstruction risk for nonlinear functions in RKHS under per-server computation and communication budgets in both quenched and annealed regimes.
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Secure Multi-User Linearly-Separable Distributed Computing
A cost-preserving transformation enforces information-theoretic secrecy in distributed computing via null-space augmentation of the allocation matrix and shared randomness injection.
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General Multi-User Distributed Computing: A Learning-Theoretic RKHS Framework for Generic Nonlinear Target Functions with Topology-Aware Risk Analysis
The GMUDC framework provides topology-aware upper and lower bounds on reconstruction risk for nonlinear functions in RKHS under per-server computation and communication budgets in both quenched and annealed regimes.