… dropouts and stragglers in largescale federated systems due to network, communication, and
… 5) Temporal Adaptability: It refers to the ability of a PFL system to learn from nonstationary …

… In this work, therefore we focus on the resource-constrained federated learning within the
DT-… This adaptability allows our model to accommodate better the dynamic changes and …

… of collaborative data sharing in vehicular edge networks (VENs) with the deployment of AI-…
sharing scheme with deep Q-network and federated learning to ensure efficient and secure …

… In this paper, we show that using Attention in Federated Learning (FL) is an efficient way of
handling concept drifts. We use a 5G network traffic dataset to simulate concept drift and test …

… congestion, leakage of personalization, and insufficient use of network resources. To address
these issues, federated learning (FL) is introduced by offering a systematical framework …

… ABSTRACT Federated learning (FL) is a new technology that has been a hot research topic.
It … Liang, and DI Kim, ‘‘Incentive design for efficient federated learning in mobile networks: …

… The operation control functions rely on as granted by federated learning. The determines
the adaptable conditions for maximizing and for the varying . Therefore the ̿ matching and …

… Such a learning solution is highly adaptable to the energy-trade … In [11], a federated energy
demand learning solution is … prediction with federated learning for electric vehicle networks,” …

… data heterogeneity and achieves efficient, adaptable client clustering. Firstly, to … federated
learning framework that integrates global and intra-cluster knowledge through neural network …

… Federated Learning within mobile edge networks.The authors in [42] presented a survey on
the threats to Federated Learning… models for dynamic adaptability without compromising the …