Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14390 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929550145880064 |
|---|---|
| author | Zhang, Boning Liu, Dongzhu Simeone, Osvaldo Wang, Guanchu Pezaros, Dimitrios Zhu, Guangxu |
| author_facet | Zhang, Boning Liu, Dongzhu Simeone, Osvaldo Wang, Guanchu Pezaros, Dimitrios Zhu, Guangxu |
| contents | To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantification is particularly important in personalized federated learning (PFL), as participating clients typically have small local datasets, making it difficult to unambiguously determine optimal model parameters. Bayesian PFL (BPFL) methods can potentially enhance calibration, but they often come with considerable computational and memory requirements due to the need to track the variances of all the individual model parameters. Furthermore, different clients may exhibit heterogeneous uncertainty levels owing to varying local dataset sizes and distributions. To address these challenges, we propose LR-BPFL, a novel BPFL method that learns a global deterministic model along with personalized low-rank Bayesian corrections. To tailor the local model to each client's inherent uncertainty level, LR-BPFL incorporates an adaptive rank selection mechanism. We evaluate LR-BPFL across a variety of datasets, demonstrating its advantages in terms of calibration, accuracy, as well as computational and memory requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14390 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning Zhang, Boning Liu, Dongzhu Simeone, Osvaldo Wang, Guanchu Pezaros, Dimitrios Zhu, Guangxu Machine Learning To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantification is particularly important in personalized federated learning (PFL), as participating clients typically have small local datasets, making it difficult to unambiguously determine optimal model parameters. Bayesian PFL (BPFL) methods can potentially enhance calibration, but they often come with considerable computational and memory requirements due to the need to track the variances of all the individual model parameters. Furthermore, different clients may exhibit heterogeneous uncertainty levels owing to varying local dataset sizes and distributions. To address these challenges, we propose LR-BPFL, a novel BPFL method that learns a global deterministic model along with personalized low-rank Bayesian corrections. To tailor the local model to each client's inherent uncertainty level, LR-BPFL incorporates an adaptive rank selection mechanism. We evaluate LR-BPFL across a variety of datasets, demonstrating its advantages in terms of calibration, accuracy, as well as computational and memory requirements. |
| title | Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.14390 |