A General and Efficient Federated Split Learning with Pre-trained Image Transformers for Heterogeneous Data

Core Concepts

Utilizing Pre-trained Image Transformers in Federated Split Learning improves model robustness and training efficiency.

Abstract

The content discusses the concept of Federated Split Learning (FSL) and its application with Pre-trained Image Transformers (PITs) to enhance model privacy and reduce training overhead. It introduces FES-PIT and FES-PTZO algorithms, highlighting their effectiveness in real-world datasets. The paper systematically evaluates FSL methods with PITs in various scenarios, emphasizing the importance of data heterogeneity challenges. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets demonstrate the superior performance of FES-PIT and FES-PTZO compared to baseline methods. Directory: Abstract Introduces Federated Split Learning (FSL) with Pre-trained Image Transformers. Introduction Discusses the significance of FL and SL paradigms in distributed learning. Motivation Highlights the resource requirements when training ViTs from scratch. Contribution Summarizes the main contributions of incorporating PITs into FSL scenarios. Methodology Defines the problem setup for FSL with pre-trained image Transformers. Experiments Details experimental setup, datasets used, models evaluated, and performance comparisons. Conclusion Concludes by emphasizing the importance of leveraging LLMs in FSL.

Stats

"Empirically, we are the first to provide a systematic evaluation of FSL methods with PITs in real-world datasets." "Our experiments verify the effectiveness of our algorithms."

Quotes

"We are the first to evaluate FSL performance with multiple PIT models in terms of model accuracy and convergence under various heterogeneous data distributions." "Our experiments verify the effectiveness of our algorithms."

Key Insights Distilled From

A General and Efficient Federated Split Learning with Pre-trained Image Transformers for Heterogeneous Data

by Yifan Shi,Yu... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16050.pdf

Deeper Inquiries

How can federated split learning address data heterogeneity challenges effectively?

Federated Split Learning (FSL) addresses data heterogeneity challenges effectively by combining the strengths of Federated Learning (FL) and Split Learning (SL). FSL splits the whole model into multiple parts, with client-side models residing locally and a server-side model. This approach allows for parallel training between clients and the server, similar to FL but with reduced resource overhead for each client, especially in resource-constrained environments like Internet of Things (IoT). In the context provided, FSL tackles data heterogeneity challenges by incorporating Vision Transformers (ViTs), which are capable of encoding long-range dependencies between input sequences. By leveraging pre-trained Image Transformers (PITs), FES-PIT accelerates the training process and improves model robustness. Additionally, techniques like FES-PTZO use zeroth-order optimization to approximate gradients efficiently in scenarios where gradient information is unavailable. Overall, through these strategies that optimize communication efficiency, enhance privacy preservation, and improve convergence on heterogeneous data distributions such as non-IID settings with different partitions, federated split learning proves effective in addressing data heterogeneity challenges.

What are potential drawbacks or limitations of using pre-trained image transformers in federated split learning?

While using pre-trained image transformers offers several advantages in federated split learning scenarios, there are also potential drawbacks and limitations to consider: Resource Intensive Training: Fine-tuning large language models from scratch can be computationally expensive and may require significant resources. Privacy Concerns: Pre-trained models may contain sensitive information from their training datasets that could pose privacy risks when used across multiple clients in a federated setting. Model Compatibility: Ensuring compatibility between pre-trained transformer architectures and specific tasks or datasets within a distributed environment can be challenging. Limited Flexibility: Pre-trained models may not always adapt well to new or specialized tasks without extensive retraining or fine-tuning. Communication Overhead: Transmitting large pre-trained model parameters over network connections during federated learning rounds can lead to increased communication costs. Gradient Inversion Attacks: Utilizing pre-trained models could potentially make systems more vulnerable to gradient inversion attacks if proper security measures are not implemented.

How might advancements in zeroth-order optimization impact future developments in federated learning?

Advancements in zeroth-order optimization have the potential to significantly impact future developments in federated learning by addressing key challenges related to optimizing complex objective functions without direct access to gradients: Efficient Gradient Approximation: Zeroth-order methods enable efficient approximation of gradients using only function evaluations rather than explicit gradient calculations. Improved Convergence: By providing an alternative way to estimate gradients accurately even under noisy conditions or limited information settings, zeroth-order optimization techniques can help improve convergence rates during training processes. Enhanced Privacy Preservation: Zeroth-order methods offer opportunities for enhancing privacy preservation mechanisms by reducing reliance on full-gradient computations that may expose sensitive information. 4 .Scalability: These advancements can contribute towards making distributed machine learning systems more scalable by reducing computational complexity while maintaining performance levels across diverse datasets and computing environments. These advancements pave the way for more robust and efficient implementations of algorithms within federate-learning frameworks while ensuring better performance outcomes under various constraints such as limited communication bandwidth or decentralized setups."