arXiv:2602.02899v2 Announce Type: replace Abstract: Decentralized training is often regarded as inferior to centralized training because the consensus errors between workers are thought to undermine convergence and generalization. This work challenges this view by introducing decentralized SGD with Adaptive Consensus (DSGD-AC), which uses a time-dependent scaling mechanism to maintain consensus errors throughout the training. We show that adaptive consensus changes the stationary variance of disagreement modes by balancing two effects: it preserves consensus-error magnitude through weaker grap
The continuous drive for more efficient and robust distributed machine learning necessitates innovations in decentralized training methodologies.
Improved decentralized AI training techniques can enhance model performance, reduce computational costs, and increase privacy in various AI applications.
This research suggests that decentralized training, previously seen as inferior, can find better solutions (flatter minima) than centralized methods, challenging established views.
- · Distributed AI platforms
- · Privacy-focused AI applications
- · Federated learning initiatives
- · Researchers in distributed optimization
- · Legacy centralized training infrastructure
- · AI projects solely reliant on centralized data
Decentralized SGD becomes a more viable and potentially superior training paradigm for large-scale AI models.
Increased adoption of decentralized AI architectures could lead to more resilient and geographically distributed AI capabilities.
This could accelerate the development of AI agents capable of learning and adapting across diverse, distributed data sources without central oversight.
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Read at arXiv cs.LG