FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning Article Swipe
Seongyoon Kim
,
Minchan Jeong
,
Sungnyun Kim
,
Sung‐Woo Cho
,
Sumyeong Ahn
,
Se-Young Yun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.02355
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.02355
Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with heterogeneous, non-iid data distribution. A key challenge in FL is client drift, where data heterogeneity impedes the aggregation of scattered knowledge. Recent studies have tackled the client drift issue by identifying significant divergence in the last classifier layer. To mitigate this divergence, strategies such as freezing the classifier weights and aligning the feature extractor accordingly have proven effective. Although the local alignment between classifier and feature extractor has been studied as a crucial factor in FL, we observe that it may lead the model to overemphasize the observed classes within each client. Thus, our objectives are twofold: (1) enhancing local alignment while (2) preserving the representation of unseen class samples. This approach aims to effectively integrate knowledge from individual clients, thereby improving performance for both global and personalized FL. To achieve this, we introduce a novel algorithm named FedDr+, which empowers local model alignment using dot-regression loss. FedDr+ freezes the classifier as a simplex ETF to align the features and improves aggregated global models by employing a feature distillation mechanism to retain information about unseen/missing classes. Consequently, we provide empirical evidence demonstrating that our algorithm surpasses existing methods that use a frozen classifier to boost alignment across the diverse distribution.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.02355
- https://arxiv.org/pdf/2406.02355
- OA Status
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- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4399421987Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.02355Digital Object Identifier
- Title
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FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-04Full publication date if available
- Authors
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Seongyoon Kim, Minchan Jeong, Sungnyun Kim, Sung‐Woo Cho, Sumyeong Ahn, Se-Young YunList of authors in order
- Landing page
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https://arxiv.org/abs/2406.02355Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2406.02355Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2406.02355Direct OA link when available
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Feature (linguistics), Regression, Distillation, Computer science, Artificial intelligence, Machine learning, Chromatography, Statistics, Chemistry, Mathematics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.significant | 57 |
| abstract_inverted_index.distillation | 194 |
| abstract_inverted_index.personalized | 19, 153 |
| abstract_inverted_index.(personalized | 21 |
| abstract_inverted_index.Consequently, | 202 |
| abstract_inverted_index.demonstrating | 207 |
| abstract_inverted_index.distribution. | 29, 225 |
| abstract_inverted_index.heterogeneity | 40 |
| abstract_inverted_index.overemphasize | 111 |
| abstract_inverted_index.dot-regression | 171 |
| abstract_inverted_index.heterogeneous, | 26 |
| abstract_inverted_index.representation | 131 |
| abstract_inverted_index.unseen/missing | 200 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |