Stability and Generalization for Decentralized Markov SGD
Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. In many practical applications, data are generated by Markov chains and learning is performed in a decentralized manner, which introduces significant analytical challenges. In this work, we investigate the stability and generalization of decentralized stochastic gradient descent (SGD) and stochastic gradient descent ascent (SGDA) under Markov chain sampling. Leveraging a stability-based framework, we characterize how Markovian dependence and decentralized communication jointly influence generalization behavior. Our analysis captures the eff...
| Authors | Jiahuan Wang, Ziqing Wen, Ping Luo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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