SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
In the rapidly evolving field of artificial intelligence, Federated Learning (FL) has emerged as a vital approach, allowing multiple parties to collaboratively train machine learning models while maintaining the privacy of their individual datasets. However, the practical implementation of FL is often hindered by both system and statistical heterogeneity across clients. A recent paper, titled “SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport,” seeks to address these challenges through innovative methodologies.
Overview of Federated Learning Challenges
Federated Learning offers a promising solution to privacy concerns, but it is not without its limitations. The following key challenges are prevalent:
- Server-side pruning: While this method can improve model efficiency, it often lacks the necessary personalization for individual clients.
- Client-side pruning: This approach can be computationally prohibitive, especially for resource-constrained devices, making it less feasible for widespread use.
- Parametric divergence: The pruning process can lead to significant variations among heterogeneous submodels, destabilizing the training process and hindering the convergence of the global model.
Introducing SubFLOT
To tackle these issues, the authors propose SubFLOT, a novel framework designed to enhance server-side personalized federated pruning. SubFLOT introduces two key components:
- Optimal Transport-enhanced Pruning (OTP) module: This module treats historical client models as proxies for local data distributions. By formulating the pruning task as a Wasserstein distance minimization problem, SubFLOT generates customized submodels without the need to access raw data, effectively maintaining privacy while enhancing personalization.
- Scaling-based Adaptive Regularization (SAR) module: To combat parametric divergence, the SAR module adaptively penalizes deviations of a submodel from the global model. This penalty is scaled based on the client’s pruning rate, ensuring that the model remains aligned with global training objectives.
Experimental Results
The authors conducted comprehensive experiments to evaluate the effectiveness of SubFLOT. The results demonstrate that SubFLOT consistently outperforms existing state-of-the-art methods in various scenarios. The findings highlight its potential for deploying efficient and personalized models, particularly on resource-constrained edge devices.
Conclusion
SubFLOT represents a significant advancement in the field of Federated Learning, providing a robust solution to the challenges of personalization and model convergence. By leveraging optimal transport techniques and adaptive regularization, this framework not only enhances model efficiency but also preserves the privacy of client data. As the demand for privacy-preserving machine learning continues to grow, SubFLOT may pave the way for more effective implementations of Federated Learning across diverse applications.
For more detailed information, the full paper can be accessed on arXiv under the identifier arXiv:2604.06631v1.