arXiv:2509.08726v3 Announce Type: replace-cross Abstract: This paper focuses on the decentralized stochastic optimization problem $f(\mathbf{x})=\frac{1}{m}\sum_{i=1}^m f_i(\mathbf{x})$ over a connected network of $n$ agents, where each local function has the form of $f_i(\mathbf{x}) = {\mathbb E}\left[F(\mathbf{x};{\boldsymbol \xi}_i)\right]$ which satisfies the $(L_0,L_1)$-smooth condition but possibly nonconvex and each random variable ${\boldsymbol \xi}_i$ follows distribution ${\mathcal D}_i$. We propose a novel algorithm called decentralized normalized stochastic gradient descent (DNSGD)
The continuous advancements in distributed computing and machine learning research make decentralized optimization a current focus for addressing scalability and privacy in AI.
Decentralized optimization is crucial for developing robust, scalable, and privacy-preserving AI systems, moving computation and learning closer to data sources at the edge.
This research introduces a novel algorithm that improves the efficiency and stability of decentralized stochastic nonconvex optimization, particularly relevant for AI applications with non-smooth functions and distributed data.
- · Edge AI providers
- · Distributed computing platforms
- · Organizations with sensitive decentralized data
- · Centralized cloud AI services
Improved performance and reliability of decentralized AI model training.
Accelerated adoption of federated learning approaches in sensitive sectors like healthcare and finance.
Reduced reliance on large, centralized data centers for AI model development and deployment, distributing compute power more widely.
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