Federated Dual Prompt Tuning: Overcoming Domain Shift and Improving Communication Efficiency in Federated Learning

Core Concepts

Federated Dual Prompt Tuning (Fed-DPT) is a novel federated learning approach that leverages prompt tuning techniques for both visual and textual inputs to address the challenges of domain shift and communication efficiency in federated learning.

Abstract

The paper introduces Federated Dual Prompt Tuning (Fed-DPT), a novel federated learning method that addresses the challenges of domain shift and communication efficiency.
Key highlights:

Fed-DPT employs a pre-trained CLIP model and utilizes both visual and textual prompt tuning techniques to facilitate domain adaptation over decentralized data.
It introduces domain-specific prompts and couples visual and textual representations through self-attention to tackle the challenge of domain shift across clients.
The parameter-efficient prompt tuning approach significantly reduces communication costs compared to fine-tuning the entire model.
Extensive experiments on domain adaptation benchmarks demonstrate the effectiveness of Fed-DPT, outperforming conventional federated learning methods and existing domain-agnostic CLIP-based approaches.
The paper first formulates the problem of domain-aware federated learning, where each client's local data originates from a different domain. It then details the Fed-DPT method, including the local training framework, parameter aggregation pipeline, and the use of momentum update to address sudden parameter changes.
The authors conduct thorough experiments on three domain adaptation datasets - DomainNet, OfficeHome, and PACS. Fed-DPT consistently achieves superior performance compared to baselines, improving the average accuracy on DomainNet by 14.8% over the original CLIP model. The paper also includes ablation studies to analyze the contributions of different components of Fed-DPT.

Stats

Fed-DPT attains 68.4% average accuracy over six domains in the DomainNet dataset, which improves the original CLIP by a large margin of 14.8%.
In the OfficeHome dataset, Fed-DPT improves the zero-shot CLIP by 4.3% average accuracy and 0.3% standard deviation over four domains.
On the PACS dataset, Fed-DPT achieves 97.2% average accuracy, outperforming the zero-shot CLIP by 1.4%.

Quotes

"Remarkably, we obtain a 68.4% average accuracy over six domains in the DomainNet dataset, outperforming the original CLIP model by 14.8%."
"Compared to conventional federated learning methods like FedAvg and FedProx, and existing domain-agnostic CLIP-based approaches such as PromptFL and FedCLIP, our Fed-DPT consistently achieves superior performance on three benchmarks."

Key Insights Distilled From

Dual Prompt Tuning for Domain-Aware Federated Learning

by Guoyizhe Wei... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2310.03103.pdf

Deeper Inquiries

How can the proposed Fed-DPT method be extended to handle more complex domain shift scenarios, such as when the data distributions across clients are not only different in the input space but also in the label space

To handle more complex domain shift scenarios where data distributions across clients differ not only in the input space but also in the label space, the Fed-DPT method can be extended by incorporating domain-specific label information. This can be achieved by introducing domain-specific classifiers or label predictors in addition to the domain-specific prompts. By training these classifiers along with the prompts in a federated learning setting, the model can learn to adapt not only to the input distribution differences but also to the label distribution variations across different domains. This approach would enable the model to better generalize and perform well in scenarios with significant differences in both input and label spaces.

What other parameter-efficient techniques, besides prompt tuning, could be explored to further reduce the communication cost in federated learning while maintaining model performance

In addition to prompt tuning, other parameter-efficient techniques that could be explored to further reduce communication costs in federated learning while maintaining model performance include:

Knowledge Distillation: Employing knowledge distillation techniques to transfer knowledge from a large, centralized model to smaller models at the client level. This can help reduce the amount of information that needs to be communicated during the aggregation process.
Sparse Model Updates: Utilizing techniques that allow for sparse model updates, where only the most relevant parameters are communicated between clients and the central server. This can help minimize the amount of data that needs to be exchanged while still preserving model performance.
Quantization and Compression: Applying quantization and compression methods to reduce the size of model updates before transmission, thereby decreasing communication overhead without significantly impacting model accuracy.
Differential Privacy: Incorporating differential privacy mechanisms to protect the privacy of individual client data while still allowing for effective model training in a federated learning setting.

Given the success of Fed-DPT in domain-aware federated learning, how could the insights from this work be applied to other federated learning settings, such as personalized federated learning or multi-task federated learning

The insights from the success of Fed-DPT in domain-aware federated learning can be applied to other federated learning settings in the following ways:

Personalized Federated Learning: By incorporating domain-specific prompts and adaptation techniques similar to Fed-DPT, personalized federated learning models can be developed. These models can adapt to individual user preferences and characteristics while preserving privacy and data locality.
Multi-Task Federated Learning: Extending the concept of domain-specific prompts to different tasks within a federated learning setting can enable multi-task learning. By leveraging shared representations and task-specific adaptations, models can learn to perform multiple tasks simultaneously across decentralized data sources. This approach can improve overall model performance and efficiency in federated learning scenarios.