FEDLOGE: Joint Local and Generic Federated Learning under Long-Tailed Data

Core Concepts

The author introduces FedLoGe, a framework that enhances both local and generic model performance in the context of Federated Long-Tailed Learning by integrating representation learning and classifier alignment within a neural collapse framework.

Abstract

FedLoGe introduces an approach to improve both local and global model performance in Federated Long-Tailed Learning by utilizing representation learning and classifier alignment. The framework consists of SSE-C for enhanced representation learning and GLA-FR for adaptive feature realignment. Experimental results on CIFAR-10/100-LT, ImageNet-LT, and iNaturalist demonstrate superior performance over state-of-the-art methods.
Key points:

FedLoGe addresses the imbalance issue in Federated Long-Tailed Learning.
The framework integrates representation learning and classifier alignment.
SSE-C is introduced for improved representation learning.
GLA-FR enables adaptive feature realignment for both global and local models.
Experimental results show significant performance gains over existing methods.

Stats

Extensive experimental results on CIFAR-10/100-LT, ImageNet-LT, and iNaturalist demonstrate the advantage of FedLoGe over state-of-the-art pFL and Fed-LT approaches.

Quotes

"Noisy features with larger variances are pruned while enhancing the quality of dominant features."
"Our investigation reveals the feasibility of employing a shared backbone as a foundational framework."

Key Insights Distilled From

FedLoGe

by Zikai Xiao,Z... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2401.08977.pdf

Deeper Inquiries

How can adaptive sparsity be incorporated into the FedLoGe framework

Adaptive sparsity can be incorporated into the FedLoGe framework by introducing a mechanism that dynamically adjusts the level of sparsity in the feature extractor based on certain criteria. This could involve monitoring the performance of the model during training and gradually increasing or decreasing the sparsity ratio to optimize performance. By adapting the level of sparsity, the model can focus on retaining important features while pruning less relevant ones, leading to more efficient representation learning and improved overall performance.

What are the potential implications of neglecting feature degeneration in federated learning

Neglecting feature degeneration in federated learning can have significant implications for model performance and generalization. When features with small means are not properly addressed, they may introduce noise into the model's representations, leading to suboptimal results. This can result in decreased accuracy, slower convergence during training, and reduced robustness when applied to new data. Neglecting feature degeneration may also hinder adaptability to diverse datasets and limit the effectiveness of personalized models tailored to specific client preferences.

How might the concept of neural collapse impact other areas beyond federated learning

The concept of neural collapse observed in federated learning could have implications beyond this specific domain. In other areas such as traditional machine learning models or deep learning applications outside federated settings, understanding how neural networks behave during training phases could lead to insights into optimization strategies, regularization techniques, and network architecture design. The phenomenon of feature collapse identified in neural networks might offer valuable lessons for improving model efficiency, reducing overfitting tendencies, enhancing interpretability of learned representations, and optimizing decision boundaries across various tasks within artificial intelligence research.