On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach
arXiv:2506.00181v2 Announce Type: replace Abstract: Distributed stochastic optimization intertwines (i) stochastic gradient noise, (ii) communication compression, and (iii) adaptive/normalized updates. While each factor has been studied in isolation, their joint effect under realistic assumptions remains poorly understood. In this work, we develop a unified theoretical framework for Distributed Compressed SGD (DCSGD) and its sign variant Distributed SignSGD (DSignSGD) under the recently introduced $(L_0, L_1)$-smoothness condition. From a conceptual perspective, we show that the first- and sec
The increasing scale and complexity of AI models necessitate more efficient and robust distributed training methods, driving research into their underlying theoretical guarantees.
Improved theoretical understanding of distributed optimization directly impacts the scalability and reliability of large-scale AI systems, which are foundational to many emerging technologies.
This research provides a more unified theoretical framework for understanding key trade-offs in distributed compressed stochastic gradient descent, potentially leading to more optimized algorithms.
- · AI researchers
- · Cloud providers
- · Large language model developers
- · AI projects with inefficient scaling
More efficient distributed training algorithms will be developed and implemented in AI frameworks.
This could lead to faster training times and reduced computational costs for complex AI models.
Lower compute barriers might democratize access to training larger models to a wider range of organizations.
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