Mitigating Dimensional Collapse in Heterogeneous Federated Learning

Conceitos Básicos

Heterogeneous data in federated learning leads to dimensional collapse in both global and local models, which can be effectively mitigated by the proposed FEDDECORR method.

Resumo

The paper studies the impact of data heterogeneity on the representations learned in federated learning. Key observations and insights:

1. Empirical observations:

• Stronger data heterogeneity among clients leads to more severe dimensional collapse in the representations of both global and local models.
• The dimensional collapse of the global model is inherited from the local models.

2. Theoretical analysis:

• Analyzes the gradient flow dynamics of local training and shows that heterogeneous data drives the weight matrices of local models to be biased towards low-rank, resulting in dimensional collapse of representations.

3. Proposed method - FEDDECORR:

• Adds a regularization term during local training to encourage the representations to be decorrelated, effectively mitigating dimensional collapse.
• FEDDECORR is computationally efficient and can be easily integrated with existing federated learning methods.

4. Experiments:

• FEDDECORR consistently outperforms baseline federated learning methods across various datasets and heterogeneity settings.
• The improvements from FEDDECORR become more pronounced as the number of clients increases, demonstrating its effectiveness in large-scale federated learning.

Estatísticas

"As the degree of data heterogeneity increases, more singular values tend to evolve towards zero."
"Heterogeneous data drive the weight matrices of the local models to be biased to being low-rank, which further results in representation dimensional collapse."

Citações

"Interestingly, we find that as the degree of data heterogeneity increases, more singular values tend to evolve towards zero. This observation suggests that stronger data heterogeneity causes the trained global model to suffer from more severe dimensional collapse, whereby representations are biased towards residing in a lower-dimensional space (or manifold)."
"Essentially, dimensional collapse is a form of oversimplification in terms of the model, where the representation space is not being fully utilized to discriminate diverse data of different classes."

Principais Insights Extraídos De

Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning

by Yujun Shi,Ji... às arxiv.org 04-09-2024

https://arxiv.org/pdf/2210.00226.pdf

Perguntas Mais Profundas

How can FEDDECORR be extended to handle other types of data heterogeneity beyond label distribution shift

FEDDECORR can be extended to handle other types of data heterogeneity beyond label distribution shift by adapting the regularization term to address different sources of heterogeneity. For example, in addition to label distribution shift, there could be feature distribution shift among clients. To handle this, FEDDECORR could incorporate a regularization term that encourages the representations of different features to be uncorrelated across clients. This would help prevent the collapse of certain dimensions in the feature space due to heterogeneity in feature distributions. By extending the regularization to consider various aspects of data heterogeneity, FEDDECORR can be more versatile in mitigating dimensional collapse in federated learning settings with diverse types of data heterogeneity.

What are the potential drawbacks or limitations of the FEDDECORR approach, and how can they be addressed

One potential drawback of the FEDDECORR approach is the need to tune the regularization coefficient, β. If β is not appropriately chosen, it may lead to suboptimal performance or even hinder the learning process. To address this limitation, automated methods such as hyperparameter optimization techniques or adaptive learning rate schedules can be employed to dynamically adjust β during training based on the model's performance. This adaptive tuning can help ensure that FEDDECORR effectively mitigates dimensional collapse without the need for manual fine-tuning.
Another limitation could be the computational overhead introduced by the additional regularization term. To address this, techniques such as mini-batch processing, parallel computing, or model compression methods can be applied to reduce the computational burden of FEDDECORR. By optimizing the implementation of the regularization term, the overall efficiency of the federated learning process can be maintained while still benefiting from the dimensional collapse mitigation provided by FEDDECORR.

Can the insights from this work on dimensional collapse be applied to improve representation learning in other distributed learning settings beyond federated learning

The insights from this work on dimensional collapse can be applied to improve representation learning in other distributed learning settings beyond federated learning by incorporating similar regularization techniques to prevent dimensional collapse. For example, in multi-party learning scenarios where multiple parties collaborate to train a shared model, the concept of dimensional collapse and the mitigation strategies proposed in FEDDECORR can be adapted to ensure that the learned representations are diverse and informative. By applying similar regularization methods to encourage diverse representations and prevent oversimplification, the quality and robustness of the learned models can be enhanced in various distributed learning settings.