… collaborative machine learning of complex models among distributed devices, a decentralized
ML approach called Federated Learning (FL) is introduced in [21]. In FL, mobile devices …

… intensive resources of mobile clients in … of federated learning in mobile edge computing,
and then investigates the state-of-the-art resource optimization approaches in federated learning…

… the current deep learning techniques, we propose to integrate the Deep … Learning techniques
and Federated Learning framework with mobile edge systems, for optimizing mobile edge …

… Abstract—Federated learning (FL) is a distributed learning paradigm that enables a large
number of mobile devices to collaboratively learn a model under the coordination of a central …

… cost of federated learning, we provide a framework of deploying federated learning in the …
Compared with conventional centralized learning and existing optimized federated learning …

… Abstract-We envision a mobile edge computing (MEC) framework for machine learning (ML) …
aims to extend Federated Learning (FL), a decentralized learning framework that enables …

… on federated averaging (FedAvg) algorithm, mobile devices … by edge computing, we
proposed edge federated learning (… completed independently by mobile devices. The outputs of …

… Although federated learning brings great benefits for mobile networks, it is still susceptible
to various adversarial attacks in its primary stage. That is, during a federated learning process, …

… latency but low-accuracy edge-based FL, this paper proposes a new FL framework based
on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to …

… ABSTRACT Federated Learning (FL) has recently attracted considerable attention in internet
of things, due to its capability of enabling mobile clients to collaboratively learn a global …