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This research aims to analyze the generalization performance of distributed minimax optimization algorithms, particularly focusing on stability and optimal hyperparameter choices.

March 28, 2026IEEE Transactions on Pattern Analysis and Machine Intelligence

Stability and Generalization for Distributed SGDA

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This research aims to analyze the generalization performance of distributed minimax optimization algorithms, particularly focusing on stability and optimal hyperparameter choices.
Proposed a stability-based generalization analytical framework for Distributed-SGDA.
Unified analysis of Local-SGDA and Local-DSGDA algorithms across various settings.
Comprehensive examination of stability error, generalization gap, and population risk metrics.
Revealed a trade-off between the generalization gap and optimization error.
Provided recommendations for optimal hyperparameter choices.
Validated the theoretical findings through numerical experiments for Local-SGDA and Local-DSGDA.

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Implication

Analysis reveals the trade-off between generalization and optimization errors in distributed minimax optimization methods.

Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/69c76fff8bbfbc51511e054c https://doi.org/10.1109/tpami.2026.3677027

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Stability and Generalization for Distributed SGDA

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Implication

Cite This Study

Stability and Generalization for Distributed SGDA

Key Points

Discussion

Implication

Cite This Study

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