Enabling Large Pre-Trained Models in Federated Learning with Efficient Parameter Tuning

핵심 개념

By only locally tuning and globally sharing a small portion of the model weights, FedPEFT can significantly reduce the total communication overhead while maintaining competitive or even better performance in a wide range of federated learning scenarios.

초록

The paper introduces FedPEFT, a new federated learning framework that simultaneously addresses data heterogeneity and communication challenges. FedPEFT enables the leveraging of strong pre-trained models in FL while maintaining an extremely low communication cost.
The key highlights and insights are:

Communication Analysis:

FedPEFT methods achieve better results compared to full fine-tuning and other baselines, even with significantly fewer communicated parameters.
Full fine-tuning requires orders of magnitude more communication to achieve comparable results with FedPEFT.
FedPEFT-Bias stands out as the most efficient prototype.

Capability Analysis:

As the domain gap between the pre-training dataset and downstream task increases, full fine-tuning falls further behind FedPEFT, unable to keep up despite a massive communication budget.
FedPEFT approaches can suitably adapt the upstream representations without excessively damaging them, with FedPEFT-Prompt showing the strongest robustness.
FedPEFT achieves comparable results to full fine-tuning with less than 0.3% of the trainable parameters across various federated learning settings.

Robustness Analysis:

Under differential privacy constraints, full fine-tuning experiences the sharpest drop in performance, falling lower than all FedPEFT prototypes.
In low-data regimes, FedPEFT outperforms full fine-tuning and head-tuning, revealing its capability to appropriately adapt pre-trained representations.
Overall, FedPEFT provides a new paradigm for practical and effective federated learning systems by enabling the use of large pre-trained models while significantly reducing communication costs.

통계

The communication cost per communication round for full fine-tuning is even higher than the total communication cost for FedPEFT to converge to similar final server accuracy.
FedPEFT reduces the size of communication each round from 328MB (85.88M parameters)/Client to 0.68MB (0.17M parameters)/Client when using a pre-trained ViT-Base as the backbone.

인용구

"By only locally tuning and globally sharing a small portion of the model weights, significant reductions in the total communication overhead can be achieved while maintaining competitive or even better performance in a wide range of federated learning scenarios, providing insight into a new paradigm for practical and effective federated systems."

핵심 통찰 요약

Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning

by Guangyu Sun,... 게시일 arxiv.org 04-04-2024

https://arxiv.org/pdf/2210.01708.pdf

더 깊은 질문

How can the FedPEFT framework be extended to other domains beyond computer vision, such as natural language processing or speech recognition

The FedPEFT framework can be extended to other domains beyond computer vision by adapting the parameter-efficient fine-tuning approach to suit the specific characteristics of those domains. For natural language processing (NLP), the pre-trained language models like BERT or GPT can be used as the backbone, and the fine-tuning methods can be adjusted to work with the different layers and structures of these models. For example, in NLP tasks, the fine-tuning methods can focus on adjusting the attention mechanisms or specific layers of the pre-trained model while keeping other parts frozen. This targeted fine-tuning can help preserve the high-level semantics of the pre-trained model while adapting it to the new task. Similarly, in speech recognition, the FedPEFT framework can be applied by fine-tuning specific components of pre-trained speech recognition models, such as the acoustic models or language models, to optimize performance in federated learning scenarios.

What are the potential security and privacy implications of the parameter-efficient fine-tuning approach used in FedPEFT, and how can they be further addressed

The parameter-efficient fine-tuning approach used in FedPEFT may have potential security and privacy implications, especially in federated learning settings where data privacy is a primary concern. One potential risk is the leakage of sensitive information through the gradients shared during the training process. Adversaries could potentially reconstruct original training data from these gradients, compromising the privacy of the individual client data. To address these security and privacy implications, additional measures can be implemented. Differential privacy techniques, such as adding noise to the gradients or applying differential privacy mechanisms during the training process, can help protect against privacy breaches. Secure aggregation protocols can also be employed to ensure that the model updates are combined in a privacy-preserving manner. Furthermore, robust encryption methods and secure communication channels can be utilized to safeguard the transmission of sensitive information during the federated learning process.

Given the insights on the impact of domain gap, how can the FedPEFT framework be adapted to handle more diverse and dynamic domain shifts in real-world federated learning deployments

To handle more diverse and dynamic domain shifts in real-world federated learning deployments, the FedPEFT framework can be adapted by incorporating adaptive fine-tuning strategies. One approach is to implement dynamic fine-tuning mechanisms that adjust the fine-tuning process based on the degree of domain gap observed in the data. This adaptive fine-tuning can involve automatically selecting the most suitable fine-tuning method (e.g., Bias, Adapter, or Prompt) based on the characteristics of the data distribution and domain shift. Additionally, continual learning techniques can be integrated into the FedPEFT framework to enable the model to adapt and evolve over time as it encounters new data distributions and domain shifts. By incorporating adaptive and continual learning strategies, FedPEFT can enhance its robustness and flexibility in handling diverse and dynamic domain shifts in real-world federated learning scenarios.