Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data
Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical challenge of data heterogeneity, and there is limited theoretical understanding of how different D-SSL frameworks respond to this challenge. To fill this gap, we present a rigorous theoretical analysis of the robustness of D-SSL frameworks under non-IID (non-independent and identically distributed) settings. Our results show that pre-training with Masked Image Modeling (MIM) is inherently more robust to heterogeneous
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- LinkedLinked via arxiv author · 85%Xuanyu Chen →
“Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data”
- LinkedLinked via arxiv author · 85%Nan Yang →
“Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data”
- LinkedLinked via arxiv author · 85%Shuai Wang →
“Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data”
- LinkedLinked via arxiv author · 85%Dong Yuan →
“Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Understanding the Robustness of Distributed Self-Supervised ” ≈ “aymericdamien/TopDeepLearning””