A communication-efficient distributed algorithm is proposed for fixed-point seeking of biased stochastic operators using inexact iterations, compression, and period skipping, with convergence shown under relaxed conditions and unified with non-convex optimization.
Convergence in High Probability of Distributed Stochastic Gradient Descent Algorithms , year=
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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.
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Distributed Seeking for Fixed Points of Biased Stochastic Operators: A Communication-Efficient Approach
A communication-efficient distributed algorithm is proposed for fixed-point seeking of biased stochastic operators using inexact iterations, compression, and period skipping, with convergence shown under relaxed conditions and unified with non-convex optimization.
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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.