KoReA-SFL: A Multi-Model Federated Learning Approach with Knowledge Replay to Mitigate Catastrophic Forgetting

Core Concepts

KoReA-SFL adopts a multi-model aggregation mechanism and a knowledge replay strategy to address the issues of gradient divergence caused by non-IID data and catastrophic forgetting in Split Federated Learning.

Abstract

The paper proposes a novel Split Federated Learning (SFL) approach called KoReA-SFL to address the challenges of data heterogeneity and catastrophic forgetting in SFL.
Key highlights:

KoReA-SFL maintains multiple branch server-side and client-side model portions to enable local training and knowledge sharing among the branch models. The branch models are aggregated with a master model to alleviate gradient divergence.
To mitigate catastrophic forgetting, KoReA-SFL employs a knowledge replay strategy. The main server selects inactivated clients as assistant clients and requests them to upload features of specific categories to assist in training the server-side portions.
KoReA-SFL adopts an adaptive sampling proportion adjustment mechanism to balance the accuracy improvement and communication overhead.
Experimental results on various datasets and models demonstrate that KoReA-SFL significantly outperforms conventional SFL methods, especially in non-IID scenarios.

Stats

The variance of stochastic gradients is upper bounded by σ^2.
The expectation of squared norm of stochastic gradients is upper bounded by G^2.
The Federated Gradient Norm (FGN) is used to approximate the curvature of the loss landscape during training.

Quotes

"To alleviate catastrophic forgetting, the cloud server can request inactivated clients to upload a small number of missing category features to assist in training of the specific server-side portion."
"KoReA-SFL adopts a dynamic sampling proportion adjustment mechanism. Previous work observed that when the curvature of the loss landscape at a particular point w is large, model training is in a critical learning period."

Key Insights Distilled From

KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting

by Zeke Xia,Min... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12846.pdf

Deeper Inquiries

How can the knowledge replay strategy be extended to handle more complex data distributions, such as evolving or drifting data distributions

The knowledge replay strategy can be extended to handle more complex data distributions, such as evolving or drifting data distributions, by incorporating adaptive mechanisms and continuous learning strategies.

Adaptive Sampling Proportion Adjustment: Instead of using a fixed sampling proportion, the strategy can be enhanced to dynamically adjust the sampling proportion based on the changing data distribution. By monitoring the data distribution shifts over time, the system can adaptively increase or decrease the sampling proportion for different categories or data subsets.

Incremental Knowledge Replay: Implementing an incremental learning approach where the system continuously updates the knowledge replay process based on new data instances. This way, the system can adapt to evolving data distributions and incorporate new information into the knowledge replay strategy.

Drift Detection and Correction: Integrate drift detection mechanisms to identify when the data distribution significantly changes or drifts. When drift is detected, the knowledge replay strategy can be triggered to focus more on the new data distribution while still retaining knowledge from the previous distributions.

Ensemble Knowledge Replay: Utilize ensemble techniques where multiple knowledge replay models are maintained, each capturing different aspects of the data distribution. By combining the outputs of these models, the system can handle complex and evolving data distributions more effectively.

What are the potential drawbacks or limitations of the multi-model aggregation approach, and how can they be addressed

The multi-model aggregation approach in KoReA-SFL may have potential drawbacks or limitations that need to be addressed:

Increased Model Complexity: Maintaining multiple models and aggregating them can increase the overall complexity of the system, leading to higher computational costs and resource requirements. This can impact scalability and efficiency.

Model Synchronization Challenges: Ensuring synchronization and consistency among multiple models during aggregation can be challenging, especially in dynamic and distributed environments. Inconsistencies or delays in model updates can affect the overall performance.

Overfitting Risk: Aggregating multiple models may increase the risk of overfitting, especially if the models are not diverse enough or if the aggregation process is not properly regularized. This can lead to reduced generalization performance.

To address these limitations, the following strategies can be implemented:

Regularization Techniques: Apply regularization methods to prevent overfitting and ensure that the aggregated model generalizes well to unseen data. Techniques like dropout, weight decay, or ensemble learning can help mitigate overfitting risks.

Model Diversity: Ensure diversity among the individual models by introducing randomness or diversity in the training process. This can be achieved through different initialization strategies, data sampling techniques, or model architectures.

Efficient Aggregation Algorithms: Develop efficient and robust aggregation algorithms that can handle model synchronization challenges effectively. Techniques like Federated Averaging with momentum or differential privacy can improve the aggregation process.

How can the proposed techniques in KoReA-SFL be applied to other federated learning scenarios beyond split federated learning, such as cross-device federated learning or federated learning with differential privacy

The techniques proposed in KoReA-SFL can be applied to other federated learning scenarios beyond split federated learning, such as cross-device federated learning or federated learning with differential privacy, by adapting the strategies to suit the specific requirements of each scenario:

Cross-Device Federated Learning: In cross-device federated learning, where devices have varying capabilities and data distributions, the knowledge replay strategy can be extended to consider the unique characteristics of each device. By incorporating device-specific knowledge replay mechanisms, the system can optimize model training across diverse devices.

Federated Learning with Differential Privacy: For federated learning with differential privacy, the techniques in KoReA-SFL can be enhanced to ensure privacy-preserving knowledge sharing. By integrating differential privacy mechanisms into the knowledge replay process and aggregation steps, the system can protect sensitive information while still achieving collaborative model training.

Dynamic Data Partitioning: Implement dynamic data partitioning strategies to adapt to the privacy and data distribution requirements of different federated learning scenarios. By dynamically adjusting the data partitioning and model aggregation based on the specific constraints of each scenario, the system can optimize performance while maintaining data privacy and security.