… Federated learning (FL) paradigm prevents the legacy pre… to cloud servers by enabling
federated averaging (FedAvg) … , network automation can be advanced with deep q-learning (…

… a lack of network adaptability. To address these issues, we propose a federated learning
framework for resource allocation in wireless networks with multiple systems. It accelerates the …

… —Federated learning enables collaborative deep learning … and communication efficient
federated learning via adaptive … concept of self-paced learning to adaptively adjust thresholds to …

… More importantly, using the same data, a multi-task deep neural network in federated
learning (MT-DNN-FL) can perform multiple anomaly detection tasks (ie, VPN traffic recognition, …

… Inspired by [14] and [15], we consider a circular small cell network with the BS located at the
center and all the UEs uniformly distributed within the coverage. Let dk denote the distance …

… network (CNN) as a ML model. Sometimes we interchangeably use the term deep
learning (DL) instead of neural network (… the learning to the number of available devices (adaptability/…

… federation has the willingness/permission to share network … the adaptability of FLAD in
adjusting its learning strategy in … This adaptability extends to various scenarios, including those …

… network … learning has emerged as the preferred method for data processing tasks, and FL
represents the latest advancement in this field. Federated learning (FL) is a machine learning …

… of FL, we show it raises specific concerns in terms of privacy, latency, and adaptability. …
on the federation. Cross-device Federated Learning (CD-FL) is a federated setting where on-…

… the rest and make the bridge adaptable to both external layers: … Federated learning workflow
reflects on FAL components and is separated into two higher-order components: Network …