Federated learning participants have two identities: model trainers and model users. As model users, participants care most about the performance of the final model on their own distributions, which is called Personal Model Performance (PMP). This makes training a single global model to accommodate all participants impractical because the data distributions of participants are heterogeneous. As model trainers, due to high training costs, participants are reluctant to contribute models if incentives are not enough. With the combination of the above two reasons, we propose a dual-identity double auction as an incentive mechanism in personalized federated learning, allowing directional selection between model users and model trainers, both of which are served by FL participants. Within the double auction framework, we devise a reinforcement learning-based model selection method. This method selects a set of models for each buyer to bid on. The bought models are aggregated to be a personalized model to achieve higher PMP. Additionally, we implement a transaction partition-based approach for determining clearing prices and winning pairs. We address the challenge of the unavailability of private yet essential data distribution information, the coupled influence of model selection and auction results on PMP, and more utility improvement ways of multi-demand dual-identity participants. Finally, our double auction optimizes the PMP of all participants and ensures the truthfulness of multi-demand dual-identity participants, which is harder compared with single-demand single-identity participants.

The rise of large models, often referred to as foundational models, has led to considerable progress in the field of artificial intelligence research. Our empirical findings indicate that the large models might struggle or deliver poor performance when it comes to specific surface segmentation challenges, including the identification and segmentation of defects on strip steel surfaces (S3D) and the detection of imperfections on magnetic tile surfaces. To apply the large model to defects segmentation, rather than fine-tuning the large model, we propose Segmentation-Driven Image Enhancement (SDIE), using several classic filters to enhance the input images. In this case, the weights of the filters in multiple layers are controlled by reinforcement learning. Then, we test our method on two S3D datasets with different few-shot settings. Our method accomplishes the task brilliantly compared with other methods for S3D such as CPANet. We believe that our work not only opens up opportunities for downstream tasks such as segmenting industrial defects using large models, but may also have potential applications in various fields in the future, including medical image processing, remote sensing image analysis, agriculture and more.

Nowadays, Federated learning (FL) is popular as it achieves distributed model training while allowing data to stay locally. It trains a global model by aggregating a selected set of local models from participants' local data. However, the global model may not perform well for all participants, especially when participants' data distributions are non-IID. Participants actually care more about the Personal Model Performance (PMP), i.e., the model performance on their own data distribution, instead of the model performance on all data. In this paper, we design a model selection method to assign a personalized set of models for each participant to maximize PMP. We first propose a model selection metric, that is model similarity. We prove theoretically that selecting models similar to a participant's own local model can make the aggregated model closer to the ideal one. Then we design a DRL-based model selection method to maximize PMP for each participant. By careful design and dimension reduction of actions and states, our TD3-based model selection method achieves the highest PMP compared with baselines. Moreover, it has a transfer ability, which means a model selection agent trained on a dataset, e.g., MNIST, works well on another similar dataset, e.g., FMNIST.