Text-Enhanced Data-Free Approach for Federated Class-Incremental Learning

Core Concepts

Introducing LANDER to address catastrophic forgetting in FCIL by leveraging label text embeddings.

Abstract

The article introduces the concept of Federated Class-Incremental Learning (FCIL) and discusses the importance of Data-Free Knowledge Transfer (DFKT) in mitigating catastrophic forgetting. It presents the novel method LANDER, which utilizes label text embeddings to enhance knowledge transfer from previous models to the current model. The approach focuses on organizing high-confidence regions of client and server models effectively, utilizing LTE anchors during training phases. Extensive experiments on CIFAR100, Tiny-ImageNet, and ImageNet demonstrate superior performance compared to previous methods.
Abstract:

FCIL involves dynamic addition of new classes in federated learning.
DFKT plays a crucial role in addressing catastrophic forgetting and data privacy issues.
LANDER leverages LTE anchors for synthesizing meaningful samples.
Introduction:

FL enables collaboration without sharing personal data.
FCIL addresses emerging data classes through continual learning principles.
DFKT transfers knowledge without raw data access.
Proposed Method: LANDER:

Utilizes LTE as anchor points during training phases.
Enhances DFKT by generating more meaningful samples.
Introduces Bounding Loss to address embedding overlap.
Experiments:

Conducted on CIFAR100, Tiny-ImageNet, and ImageNet datasets.
Outperforms prior methods in accuracy and forgetting scores across different settings.

Stats

Extensive experiments conducted on CIFAR100, Tiny-ImageNet, and ImageNet demonstrate that LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL.
Each party has access to only a subset of the classes Yt (i.e., non-IID).
The scale factor αt cur and αt pre are adaptively set based on κ = log2( |Yt| + 1), δ = sqrt(|Y1:t−1| / |Yt|), where |Yt| is the number of classes in task t.

Quotes

"LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL."
"Our contributions can be summarized as follows: We propose LANDER which leverage the power of pretrained language models in FCIL."

Key Insights Distilled From

Text-Enhanced Data-free Approach for Federated Class-Incremental Learning

by Minh-Tuan Tr... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14101.pdf

Deeper Inquiries

How does using LTE as anchor points improve knowledge transfer compared to traditional methods

Using LTE as anchor points improves knowledge transfer by providing a structured and meaningful reference for organizing the latent space of the data. Traditional methods often lack this organization, leading to scattered embeddings that make it challenging to generate high-quality synthetic data. By leveraging LTE as anchors, the model can constrain feature embeddings around these points, enriching the surrounding area with more semantically meaningful information. This approach ensures that synthetic samples generated during Data-Free Knowledge Transfer (DFKT) are closer in proximity to real data distribution, thereby enhancing the effectiveness of knowledge transfer and mitigating catastrophic forgetting.

What are the implications of introducing Bounding Loss for addressing embedding overlap

Introducing Bounding Loss addresses embedding overlap by encouraging sample embeddings to remain flexible within a defined radius around anchor points rather than pushing them too close to the anchors. In heterogeneous federated settings where there may be imbalances in class representation across clients, traditional methods aiming for exact alignment with anchor points can lead to overlapping embeddings and reduced diversity in synthesized data. The Bounding Loss helps retain natural differences in sample embeddings while still ensuring they are organized around meaningful LTEs. This flexibility within a defined radius mitigates embedding overlap issues and enhances performance in FCIL tasks.

How can learnable data stats enhance privacy preservation while generating synthetic data

Learnable data stats enhance privacy preservation while generating synthetic data by eliminating the need for clients to disclose specific training dataset statistics during the generation process. Instead of relying on global training dataset statistics or random values for normalization when synthesizing images, learnable data stats allow models like LANDER to adaptively learn mean and standard deviation values from local client datasets without compromising privacy. This approach ensures that synthetic images maintain consistency with real training data distributions without requiring sensitive statistical information sharing between clients or central servers, thus improving privacy protection measures during knowledge transfer processes.