  Federated learning enables a collaborative training and optimization of
global models among a group of devices without sharing local data samples.
However, the heterogeneity of data in federated learning can lead to unfair
representation of the global model across different devices. To address the
fairness issue in federated learning, we propose a dynamic q fairness federated
learning algorithm with reinforcement learning, called DQFFL. DQFFL aims to
mitigate the discrepancies in device aggregation and enhance the fairness of
treatment for all groups involved in federated learning. To quantify fairness,
DQFFL leverages the performance of the global federated model on each device
and incorporates {\alpha}-fairness to transform the preservation of fairness
during federated aggregation into the distribution of client weights in the
aggregation process. Considering the sensitivity of parameters in measuring
fairness, we propose to utilize reinforcement learning for dynamic parameters
during aggregation. Experimental results demonstrate that our DQFFL outperforms
the state-of-the-art methods in terms of overall performance, fairness and
convergence speed.
