1. Apart from the multiple pre-trained models, FedPCL is built on the idea of prototypical learning and contrastive learning, which are not new in federated learning. 2. The performance of FedPCL heavily relies on the selection of different pre-trained models, limiting its applications to more wide areas. As shown in Table 4, the model accuracy is quite sensitive to the pre-trained models.
This work adequately addressed the limitations. The authors developed a lightweight federated learning framework to reduce the computation and communication costs and integrated pre-trained models to extract prototypes for federated aggregation. The is a new try for federated learning.
2. The performance of FedPCL heavily relies on the selection of different pre-trained models, limiting its applications to more wide areas. As shown in Table 4, the model accuracy is quite sensitive to the pre-trained models. This work adequately addressed the limitations. The authors developed a lightweight federated learning framework to reduce the computation and communication costs and integrated pre-trained models to extract prototypes for federated aggregation. The is a new try for federated learning.