Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.25467 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913067896406016 |
|---|---|
| author | Zhu, Shuchen Huang, Zhengyang Xu, Yuqi Li, Peijin |
| author_facet | Zhu, Shuchen Huang, Zhengyang Xu, Yuqi Li, Peijin |
| contents | Federated learning increasingly operates in a large-model regime where communication, memory, and computation are all scarce. Typically, non-IID client data induce drift that degrades the stability and performance of local training. Existing remedies such as SCAFFOLD introduce heterogeneity-correction mechanisms to address this challenge, but they incur substantial extra communication and memory overhead. This paper proposes a subspace optimization method for federated learning (SSF), which performs heterogeneity-corrected optimization in a low-dimensional subspace using only projected quantities, while preserving full-dimensional control information through a backfill-style update that retains residual components whenever the active subspace changes. Under standard smoothness and bounded-variance assumptions, SSF attains a non-asymptotic rate of order $\widetilde{\mathcal{O}}(1/T+1/\sqrt{NKT})$. Experiments show favorable accuracy--efficiency trade-offs under heterogeneous data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25467 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Subspace Optimization for Efficient Federated Learning under Heterogeneous Data Zhu, Shuchen Huang, Zhengyang Xu, Yuqi Li, Peijin Machine Learning Optimization and Control Federated learning increasingly operates in a large-model regime where communication, memory, and computation are all scarce. Typically, non-IID client data induce drift that degrades the stability and performance of local training. Existing remedies such as SCAFFOLD introduce heterogeneity-correction mechanisms to address this challenge, but they incur substantial extra communication and memory overhead. This paper proposes a subspace optimization method for federated learning (SSF), which performs heterogeneity-corrected optimization in a low-dimensional subspace using only projected quantities, while preserving full-dimensional control information through a backfill-style update that retains residual components whenever the active subspace changes. Under standard smoothness and bounded-variance assumptions, SSF attains a non-asymptotic rate of order $\widetilde{\mathcal{O}}(1/T+1/\sqrt{NKT})$. Experiments show favorable accuracy--efficiency trade-offs under heterogeneous data. |
| title | Subspace Optimization for Efficient Federated Learning under Heterogeneous Data |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2604.25467 |