Abstract Remote Sensing Target Fine-grained Classification (TFGC) is of great significance
in both military and civilian fields. Due to location differences, growth in data size, and …

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL)
is to decouple general knowledge (shared among clients) and client-specific knowledge, as …

Federated learning (FL) is a distributed machine learning framework in which multiple
clients update their local models in parallel and then aggregate them to generate a global …

Federated learning (FL) enables multiple IoT devices to collaboratively train a global model
without centralizing raw data. However, achieving optimal performance for each device is …

Federated learning (FL) is a distributed machine learning paradigm for edge cloud
computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively …

Deep learning models often suffer performance degradation when test data diverges from
training data. Test-Time Adaptation (TTA) aims to adapt a trained model to the test data …

Federated learning (FL) is undergoing significant traction due to its ability to perform privacy-
preserving training on decentralized data. In this work, we focus on sensitive time series …

Personalized Federated Learning (PFL) is a commonly used framework that allows clients to
collaboratively train their personalized models. PFL is particularly useful for handling …

In traditional Federated Learning approaches like FedAvg, the global model underperforms
when faced with data heterogeneity. Personalized Federated Learning (PFL) enables clients …

Medical image classification plays a crucial role in computer-aided clinical diagnosis. While
deep learning techniques have significantly enhanced efficiency and reduced costs, the …