Gradient Normalization Enables Communication-Efficient Distributed Learning under Initialization Data Heterogeneity.

Area of Science:

• Machine Learning
• Distributed Systems
• Optimization

Background:

• Communication-efficient distributed learning trains large neural networks across clients.
• Performance degrades sharply in heterogeneous environments with varied data distributions.
• Existing theories rely on restrictive assumptions about data heterogeneity.

Purpose of the Study:

• To introduce a general gradient normalization strategy for distributed learning.
• To mitigate the negative impact of data heterogeneity on algorithm performance.
• To provide theoretical guarantees for improved distributed learning.

Main Methods:

• Developed a general gradient normalization strategy.
• Integrated the strategy into various distributed learning algorithms (e.g., compressed SGD, Federated Averaging).
• Conducted theoretical analysis and extensive numerical experiments.

Main Results:

• The normalization technique effectively mitigates data heterogeneity.
• Algorithms achieve linear speedup rates under relaxed heterogeneity assumptions.
• Demonstrated practical effectiveness and theoretical validity through experiments.

Conclusions:

• Gradient normalization is a robust strategy for heterogeneous distributed learning.
• The approach enhances performance and theoretical guarantees across diverse algorithms.
• Enables efficient training of large-scale models in real-world, varied data settings.