Enhancing Local Model Diversity to Improve One-Shot Sequential Federated Learning for Non-IID Data

Grunnleggende konsepter

A novel one-shot sequential federated learning framework that enhances local model diversity to improve global model performance while reducing communication costs and mitigating the impact of non-IID data.

Sammendrag

The paper presents a novel one-shot sequential federated learning (SFL) framework called FedELMY that enhances local model diversity to improve the performance of the global model.

Key highlights:

• Addresses the challenge of non-IID data in one-shot SFL by leveraging local model diversity.
• Introduces a local model pool for each client that comprises diverse models generated during local training.
• Proposes two distance regularization terms to further enhance model diversity and mitigate the effect of non-IID data.
• Extensive experiments show FedELMY outperforms existing one-shot PFL and SFL methods on both label-skew and domain-shift tasks.
• Achieves superior performance while maintaining low communication costs compared to other methods.

Statistikk

The training datasets for CIFAR-10 and Tiny-ImageNet are partitioned into 10 clients with Dirichlet distribution β = 0.5.
The PACS and Office-Caltech-10 datasets are partitioned into 4 clients, each with a distinct domain.

Sitater

"Existing studies have shown that combining multiple networks can notably enhance the model's performance due to the inherent diversity amongst model weights."
"To overcome this challenge within the one-shot SFL framework, we propose a diversity-enhanced mechanism for model training."

Viktige innsikter hentet fra

One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity

by Naibo Wang,Y... klokken arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12130.pdf

Dypere Spørsmål

How can the proposed diversity-enhanced mechanism be extended to other federated learning settings beyond one-shot sequential learning

The diversity-enhanced mechanism proposed in the one-shot sequential federated learning framework can be extended to other federated learning settings by adapting the concept of maintaining a diverse model pool during local training. In settings where multiple communication rounds are allowed, such as traditional parallel federated learning (PFL), the idea of incorporating a model pool with diverse models generated during local training can still be applied. Each client can train multiple models with different initializations or hyperparameters, and these models can be aggregated into a model pool. By introducing distance regularization terms to enhance model diversity and mitigate the impact of non-IID data, the performance of the global model can be improved in PFL settings as well. Additionally, in decentralized federated learning scenarios, where edge devices conduct training without a central server, the diversity-enhanced mechanism can be implemented by allowing each device to maintain a diverse set of models and exchange them with neighboring devices to enhance overall model performance.

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

One potential drawback of the current diversity regularization approach is the sensitivity of the hyperparameters 𝛼 and 𝛽, which govern the effect of the distance regularization terms 𝑑1 and 𝑑2 on model training. If these hyperparameters are not properly tuned, it may lead to suboptimal model performance. To address this limitation, a more sophisticated hyperparameter optimization strategy, such as Bayesian optimization or grid search, can be employed to find the optimal values for 𝛼 and 𝛽. Additionally, the choice of distance metrics used in the regularization terms can impact the model's ability to explore diverse solutions. Experimenting with different distance metrics, such as cosine similarity or Mahalanobis distance, can help identify the most effective measure for enhancing model diversity. Moreover, incorporating adaptive or learnable hyperparameters that adjust during training based on model performance can make the approach more robust and adaptive to different datasets and scenarios.

Can the proposed framework be further improved by incorporating advanced privacy-preserving techniques to enhance its real-world applicability

The proposed framework can be further improved by incorporating advanced privacy-preserving techniques to enhance its real-world applicability. One approach is to integrate differential privacy mechanisms into the model aggregation process to protect sensitive data during communication between clients. Differential privacy adds noise to the model updates before aggregation to prevent individual data points from being exposed. Another technique is secure multi-party computation (MPC), which allows multiple parties to jointly compute a function over their private inputs without revealing the inputs to each other. By implementing MPC protocols, the model aggregation process can be performed securely without compromising data privacy. Additionally, homomorphic encryption can be used to perform computations on encrypted data, enabling secure model training without exposing raw data. By combining these advanced privacy-preserving techniques with the diversity-enhanced mechanism, the framework can ensure both model performance improvement and data privacy protection in real-world federated learning scenarios.