High probability convergence of distributed clipped stochastic gradient descent with heavy-tailed noise

Key Points

• Convergence is achieved with high probability in systems using clipped stochastic gradient descent and heavy-tailed noise, indicating strong reliability.
• Key evidence shows that under specific conditions, such as bounded gradients, robustness in learning can be maintained despite noise characteristics.
• Analysis utilizing distributed setups focuses on clipped stochastic gradient descent to address challenges posed by heavy-tailed noise in data modeling and optimization.
• Implications highlight the necessity for further exploration in real-world applications where heavy-tailed distributions might impact algorithm performance.