An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

Centrala begrepp

Leveraging pre-trained generators for efficient knowledge transfer in heterogeneous federated learning.

Sammanfattning

1. Introduction
   • Companies develop custom models due to data scarcity.
   • Traditional FL lacks personalization, leading to privacy concerns.
2. Heterogeneous Federated Learning (HtFL)
   • Addresses data and model heterogeneity.
   • Novel knowledge-sharing schemes explored beyond client models.
3. Proposed Scheme: FedKTL
   • Utilizes pre-trained generators for knowledge transfer.
   • Produces image-vector pairs tailored to clients' tasks.
4. Methodology
   • ETF classifier used for unbiased prototypes generation.
   • Domain alignment ensures valid latent vectors for image generation.
5. Experimental Results
   • FedKTL outperforms state-of-the-art methods by up to 7.31% in accuracy.
6. Impact Analysis
   • Scalable with more clients and maintains performance with increased training epochs.
7. Ablation Study
   • Removal of key components significantly impacts performance, highlighting their importance in FedKTL.

Statistik

"Results show that our upload-efficient FedKTL surpasses seven state-of-the-art methods by up to 7.31% in accuracy."
"Our FedKTL can outperform seven state-of-the-art methods by at most 7.31% in accuracy."

Citat

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An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

by Jianqing Zha... på arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15760.pdf

Djupare frågor

How does the proposed scheme address privacy concerns associated with federated learning?

The proposed scheme, FedKTL, addresses privacy concerns in federated learning through several key mechanisms. Firstly, by leveraging a pre-trained generator on the server to produce image-vector pairs related to clients' tasks, FedKTL ensures that sensitive client data is not directly shared during the knowledge transfer process. This indirect transfer of knowledge helps preserve the privacy of individual client datasets.
Secondly, FedKTL incorporates an Equiangular Tight Frame (ETF) classifier for each client model. By using ETF classifiers instead of original classifiers, biased prototypes are avoided, leading to more accurate and unbiased knowledge transfer without compromising individual client data privacy.
Additionally, FedKTL maintains all model parameters locally on clients without sharing them externally. This approach minimizes the risk of exposing sensitive information and intellectual property while still enabling effective collaborative learning among multiple clients.
Overall, FedKTL's design prioritizes data privacy by ensuring that only relevant information for knowledge transfer is exchanged between the server and clients while keeping individual datasets secure.

What are the implications of using different pre-trained generators on the effectiveness of knowledge transfer?

The choice of pre-trained generators in Federated Knowledge-Transfer Loop (FedKTL) can have significant implications for the effectiveness of knowledge transfer in heterogeneous federated learning scenarios. Different pre-trained generators may vary in their ability to generate informative images based on latent vectors provided by clients or align these generated images with class-wise centroids effectively.
For example:

Resolution and Quality: Pre-trained generators trained on high-resolution datasets like WikiArt (1024x1024) might produce more detailed and visually appealing images compared to those trained on lower-resolution datasets like AFHQv2 (512x512). Higher-quality images can lead to better alignment with class-wise centroids and improved feature extraction during local training.

Domain Adaptation: Generators trained on diverse datasets such as FFHQ-U or Benches may offer broader domain adaptation capabilities when transferring common knowledge across different domains or tasks within heterogeneous federated learning settings.

Semantic Relevance: The semantic relevance between generated images from different pre-trained models and local dataset content plays a crucial role in effective knowledge transfer. StyleGANs pretrained on specific domains might excel at generating relevant imagery for certain tasks but could struggle with others due to domain mismatch.

In essence, selecting an appropriate pre-trained generator tailored to specific task requirements can enhance overall performance in terms of accuracy gains during collaborative learning processes within heterogeneous federated environments.

How can the findings of this study be applied to real-world scenarios beyond machine learning research?

The findings from this study hold valuable insights that extend beyond machine learning research into various real-world applications:

Privacy-Preserving Collaborative Environments: The privacy-preserving techniques employed in Federated Knowledge-Transfer Loop (FedKTL) can be adapted for secure collaboration across industries where sensitive data sharing needs mitigation risks.

Cross-Domain Knowledge Transfer: The concept of transferring common global knowledge efficiently from a central source has applications outside ML contexts—such as cross-domain collaborations where expertise exchange is vital but proprietary information must remain protected.

Personalized Learning Solutions: Implementing personalized FL methods akin to pFL approaches explored here could benefit sectors requiring customized solutions tailored per user/client preferences or characteristics—like healthcare diagnostics or financial advisory services.

By translating these research outcomes into practical use cases spanning diverse fields—from cybersecurity protocols safeguarding confidential exchanges to educational platforms offering tailored curricula—the study's methodologies pave pathways towards enhanced collaboration frameworks grounded in robust security measures and optimized performance metrics suited for real-world deployment scenarios beyond traditional ML landscapes.