… learning (ML) technologies, which leverages distributed client data and computation resources
… aims to extend Federated Learning (FL), a decentralized learning framework that enables …

… resource-adaptive framework to address the system heterogeneity problem in federated
learning. … , the two most representative federated learning approaches for system heterogeneity …

Federated learning (FL) offers a promising paradigm that empowers numerous Internet of Things
(IoT) devices to implement distributed learning … mode, the heterogeneity of participating …

… , we do not involve the public dataset during federated learning (training) in the local …
heterogeneous models, labels and data distributions (with non-IID nature) in a federated learning …

… Federated learning (FL) presents an innovative framework … of hardware capabilities,
heterogeneous devices, encompassing both … ML model training across heterogeneous devices, a …

… —Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from … do not consider the heterogeneity of client resources. Due to such …

… of federated learning. Specifically, we present a heterogeneous computation and
resource allocation framework with the aid of WPT for federated learning to minimize energy …

… In this analysis, we consider the federated system to be a partial model heterogeneity. A FL
model needs to be trained for each client subset whose models are isomorphic. Through the …

… for their resource-to-accuracy in a heterogeneous setting. This means the computational …
2.1 Federated Learning We consider the popular FL setting introduced in federated averaging (…

… We show that this hierarchical federated learning (HFL) … is federated edge learning (FEEL)
[1–4], which enables ML at the network edge without offloading any data. Federated learning (…