… gradient information under federated learning, which makes it better adaptable for non-IID …
approach for vehicular edge computing in 6g communication networks,” IEEE Transactions …

Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients’ local data distribution, …

… [31], using FL for collaborative training on a simple Deep Neural Network (DNN), demonstrates
its effectiveness in improving the adaptability of intrusion detection in IoT scenarios. …

… Federated learning exhibits distinctly different characteristics … increased adaptability in the
training of machine learning … data from all clients in the federated network to establish the …

… In addition to collaborative, crosslayered operation of IDSs is necessary to detect intrusions
at perception, network and application layers of MMG. Adaptability of the detection and pre…

… learning paradigm, Federated Learning (FL) is expected to meet the ever-increasing needs
of Machine Learning (… while preserving its privacy by locally learning the models. However, …

… Background: FedAvg Structure In this paper, we discuss the distributed network as
shown in Fig. 1. It involves N edge devices as clients and one parameter server to jointly …

… Learning (DML) at network edges. Among all the existing DML paradigms, Federated Learning
(FL) … In this subsection we use ASWT to show the adaptability of Chronos compared with …

… Although the method has certain adaptability to dynamic network environment, yet the
partition process does not consider the survivability requirements of SFC, and it is difficult to …

… datasets to train each local network. Here, a common neural network model is shared for all
… training datasets at each client, which limits the adaptability of the CNN due to the lack of the …