… resource allocation to modify objectives in machine learning. Inspired by the α-fairness metric,
we propose a similarly modified objective, q-Fair Federated Learning (… of federated training…

… • We propose a resource allocation problem for FEDL over … The first two sub-problems relate
to UE resource allocation … , (ii) UE resource allocation, and (iii) hyper-learning rate and local …

… proposed a new distributed model training framework called Federated Learning (FL) [6], [7]…
in many applications, for instance, resource allocation optimization in vehicle-to-vehicle (V2V…

… can be guaranteed using a learning rate decay, despite the negative effects of the sampling
method. 2) We formulate a resource allocation problem using the proposed FL algorithm in …

… machine learning paradigm known as Federated Learning (FL), … device dropouts, the
Hierarchical Federated Learning (HFL) … This decentralized learning approach reduces the reliance …

… , we consider the computational resource control problem in the federated learning. To
the best our knowledge, this is the first to translate the federated learning to a control-theoretic …

… Abstract—Federated learning (FL) is a distributed machine learning strategy that generates
a global model by learning from multiple decentralized edge clients. FL enables on-device …

… of edge association and resource allocation, we consider a … for federated learning with
heterogeneous resources in mobile … Wang, and FR Yu, “Robust federated learning with noisy …

… to train a learning model locally. … learning frameworks is federated learning (FL) developed
in [5]. FL is a distributed machine learning method that enables users to collaboratively learn …

… , federated learning (FL) has been introduced to act as an emerging distributed machine
learning … to the wireless and computation resource constraints. [8] only focused on a practical …