Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry
arXiv:2606.11339v1 Announce Type: cross Abstract: We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized stochastic primal-dual method, and analyze it under relaxed global geometry. Under restricted secant inequality (RSI), a constant step-size yields linear contraction to an explicit neighborhood determined by gradient noise, quantization distortion, and network connectivity, while a diminishing step-size achieves O(1/k) convergence without shared-minimizer assumptions. Under Polyak-Loj
The increasing complexity and scale of AI models necessitate more efficient distributed optimization methods, especially with growing emphasis on privacy and computational efficiency in real-world applications.
This research contributes to the foundational algorithms for distributed AI training, potentially enabling more robust and resource-efficient development of large-scale AI systems, particularly in scenarios with communication constraints.
Improved theoretical guarantees and practical methods for training distributed AI models using quantized communication, which can reduce bandwidth requirements and computational load while maintaining convergence properties.
- · Distributed AI computing platforms
- · Edge AI developers
- · Researchers in AI optimization
- · Sectors with strict communication bandwidth or privacy constraints
- · Inefficient distributed training methods
- · Systems heavily reliant on high-bandwidth communication for distributed AI
More widespread adoption of distributed machine learning in resource-constrained environments due to enhanced algorithmic efficiency.
Accelerated development of AI models that can be trained across disparate, low-bandwidth networks, influencing federated learning architectures.
Potential for new business models centered around decentralized AI training and inference, reducing reliance on centralized supercomputing infrastructure.
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