The Internet of Things (IoT) has paved the way to a highly connected society where all things
are interconnected and exchanging information has become more accessible through the …

Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …

Federated learning (FL) is a machine learning technique that allows for on-site data
collection and processing without sacrificing data privacy and transmission. Heterogeneity is …

To improve the efficiency of reinforcement learning, we propose a novel asynchronous
federated reinforcement learning framework termed AFedPG, which constructs a global …

Evidence-based or data-driven dynamic treatment regimes are essential for personalized
medicine, which can benefit from offline reinforcement learning (RL). Although massive …

During the training process of federated learning models, the domain information of the
target test data on the server can differ greatly from the training data of each client, leading to …

Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing
the sample complexity of reinforcement learning tasks by exploiting information from …

Apart from the promising potential, Federated Learning (FL) faces challenges such as high
communication costs and client heterogeneity. Although numerous works have been …

Federated reinforcement learning (FedRL) enables agents to collaboratively train a global
policy without sharing their individual data. However, high communication overhead …

Traditional federated reinforcement learning methods aim to find an optimal global policy for
all agents. However, due to the heterogeneity of the environment, the optimal global policy is …