The paper presents a mechanism-driven distributed optimization method with convergence guarantees that uses shadow pricing and VCG incentives to motivate self-interested participants to collaborate on coupled problems, forming an interdependent closed loop.
IEEE Transactions on Automatic Control , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new distributed SGD algorithm integrates Paillier homomorphic encryption with heterogeneous random stepsizes and an attenuation factor to deliver privacy against honest-but-curious agents and eavesdroppers while converging almost surely to the optimum.
citing papers explorer
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Harnessing Individual Motivation for Collective Efficiency: A Mechanism-Driven Distributed Optimization Method
The paper presents a mechanism-driven distributed optimization method with convergence guarantees that uses shadow pricing and VCG incentives to motivate self-interested participants to collaborate on coupled problems, forming an interdependent closed loop.
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Privacy-Preserving Distributed Stochastic Optimization with Homomorphic Encryption and Heterogeneous Stepsizes
A new distributed SGD algorithm integrates Paillier homomorphic encryption with heterogeneous random stepsizes and an attenuation factor to deliver privacy against honest-but-curious agents and eavesdroppers while converging almost surely to the optimum.