Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization
arXiv:2607.01755v1 Announce Type: cross Abstract: In this paper, we consider the nonsmooth nonconvex decentralized optimization problem, where inter-agent communication is compressed. We propose a general framework that unifies various decentralized stochastic subgradient-type methods with unbiased compression and contractive compression with error compensation. By relating the consensus-error iterates and the averaged iterates to the trajectories of continuous-time differential inclusions, we establish global convergence for all methods encompassed by our framework when the objective function
The continuous advancements in AI and distributed systems necessitate more efficient and scalable optimization methods, especially with increasing data and model sizes.
This research provides a more robust and efficient framework for decentralized AI, which is crucial for privacy-preserving AI, robust multi-agent systems, and federated learning applications.
This framework could lead to more stable and faster training of large-scale, decentralized AI models, particularly in nonconvex optimization problems where solutions are complex.
- · Researchers in distributed AI
- · Developers of federated learning systems
- · Industries requiring privacy-preserving analytics
- · Centralized optimization approaches
Improved efficiency and scalability for training complex AI models across distributed networks.
Accelerated deployment of AI applications in sensitive sectors like healthcare and finance due to enhanced privacy and data locality.
Potential for new decentralized AI paradigms that are more resilient to single points of failure and censorship.
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Read at arXiv cs.LG