  The uneven distribution of local data across different edge devices (clients)
results in slow model training and accuracy reduction in federated learning.
Naive federated learning (FL) strategy and most alternative solutions attempted
to achieve more fairness by weighted aggregating deep learning models across
clients. This work introduces a novel non-IID type encountered in real-world
datasets, namely cluster-skew, in which groups of clients have local data with
similar distributions, causing the global model to converge to an over-fitted
solution. To deal with non-IID data, particularly the cluster-skewed data, we
propose FedDRL, a novel FL model that employs deep reinforcement learning to
adaptively determine each client's impact factor (which will be used as the
weights in the aggregation process). Extensive experiments on a suite of
federated datasets confirm that the proposed FedDRL improves favorably against
FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the
CIFAR-100 dataset, respectively.
