… resource allocation to modify objectives in machine learning. … q-Fair Federated Learning
(q-FFL), to encourage a more fair … devices in the context of federated training. Similar to the α-…

… and fairness concerns in a resource-constrained federated learning setting, in this paper, …
and coordination for the federated learning towards both resource efficiency and model fairness…

… federated learning, in this work, we propose FairFed, a novel algorithm for fairness-aware
aggregation to enhance group fairness in federated learning… for fair ML and federated learning …

… in training due to changing channel conditions. Meanwhile, we try to achieve the target of
training a fair federated learning … the number of local iterations, and allocating radio resources. …

… In this paper, we propose a novel federated learning framework, HFFL, to achieve fairness …
on their contribution levels, thereby facilitating federated learning. We first identify agents’ …

… -fair federated learning approach [7]. To mitigate the overhead of the proposed fair resource
… The q-fair FL approach requires additional computation for Hessian approximation. Table III …

… 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 …

In order to make full use of the network data and guarantee user privacy simultaneously,
federated learning (FL) is proposed to enable distributed intelligence for local nodes without …

… the qualityaware federated learning, where the individual learning quality is estimated …
learning scenario, we propose a distributed learning system named FAIR, ie, Federated leArning …

… To prevent the multiple model federated learning training process from being stuck at local
minimum points, we will add randomness in the client-task allocation process. At each local …