Yi Liu

TL;DR: Wang et al. as mentioned in this paper introduced a privacy-preserving machine learning technique named federated learning and proposed a Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU), which differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations.

TL;DR: In this paper, the integration of 6G and federated learning is discussed and potential federated Learning applications for 6G communications are provided, as well as key technical challenges, the corresponding Federated Learning methods, and open problems for future research on 6G communication.

TL;DR: This work introduces a privacy-preserving machine learning technique named federated learning (FL) and proposes an FL-based gated recurrent unit neural network algorithm (FedGRU) for traffic flow prediction (TFP) that differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism.

TL;DR: In this article, the authors proposed an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies.

TL;DR: A blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from being involved in FL is proposed, which can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.