… device ML/DL model builders, federated learning (FL) framework is applicable by modifying
… reinforcement learning (DRL)-based approaches have been applied in edge networks to …

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model
is trained with updates computed on mobile devices while keeping the data of users local. …

… We designed a federated learning system with five clients. We ran this experiment on ten
cores of “Intel(R) Xeon(R) Gold 5118 CPU @ 2.30 ​GHz,” and we initiated the weight of our …

… In our paper, we demonstrate the creation of a network flow-based Intrusion Detection … of
two machine learning techniques, namely, federated learning and active learning. The former is …

… three aspects are often considered: federated learning optimization algorithm, client selection,
… Especially in federated neural network, the parameters of deep neural network model are …

… a federated learning network deployed on several low capacity devices connected to a
wireless mesh network. … mechanisms for the configuration or adaptability of federated learning. …

… the heterogeneity among users and limited network constraints—remains to be an open …
tive federated optimization algorithm, Adp-FedProx, to achieve the optimal learning performance …

… and adaptability of graph neural networks … federated learning framework to assess the
geospatial resilience of multicommodity food flow as shown in Figure 2. The federated learning …

… federated learning, introducing a personalized joint learning … -task learning principles and
leverages neural network model … It enables the system to reach agreements adaptable to the …

… field of machine learning. However, the way of communication that adopted in federated
learning … In this article, we introduce a super-wireless-over-the-air federated learning framework …