Cross-domain Federated Adaptive Prompt Tuning for CLIP
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Federated learning (FL) allows multiple parties to collaboratively train a global model without disclosing their data. Existing research often requires all model parameters to participate in the training procedure. However, with the advent of powerful pre-trained models, it becomes possible to achieve higher performance with fewer learnable parameters in FL. In this paper, we propose a federated adaptive prompt tuning algorithm, FedAPT, for cross-domain federated image classification scenarios with the vision-language pre-trained model, CLIP, which gives play to the strong representation ability in FL. Compared with direct federated prompt tuning, our core idea is to adaptively unlock specific domain knowledge for each test sample in order to provide them with personalized prompts. To implement this idea, we design an adaptive prompt tuning module, which consists of a global prompt, an adaptive network, and some keys. The server randomly generates a set of keys and assigns a unique key to each client. Then all clients cooperatively train the global adaptive network and global prompt with the local datasets and the frozen keys. Ultimately, the global aggregation model can assign a personalized prompt to CLIP based on the domain features of each test sample. We perform extensive experiments on two multi-domain image classification datasets. The results show that FedAPT can achieve better performance with less than 10% of the number of parameters of the fully trained model, and the global model can perform well in different client domains simultaneously.
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