  Federated learning is a decentralized learning paradigm wherein a central
server trains a global model iteratively by utilizing clients who possess a
certain amount of private datasets. The challenge lies in the fact that the
client side private data may not be identically and independently distributed,
significantly impacting the accuracy of the global model. Existing methods
commonly address the Non-IID challenge by focusing on optimization, client
selection and data complement. However, most approaches tend to overlook the
perspective of the private data itself due to privacy constraints.Intuitively,
statistical distinctions among private data on the client side can help
mitigate the Non-IID degree. Besides, the recent advancements in dataset
condensation technology have inspired us to investigate its potential
applicability in addressing Non-IID issues while maintaining privacy. Motivated
by this, we propose DCFL which divides clients into groups by using the
Centered Kernel Alignment (CKA) method, then uses dataset condensation methods
with non-IID awareness to complete clients. The private data from clients
within the same group is complementary and their condensed data is accessible
to all clients in the group. Additionally, CKA-guided client selection
strategy, filtering mechanisms, and data enhancement techniques are
incorporated to efficiently and precisely utilize the condensed data, enhance
model performance, and minimize communication time. Experimental results
demonstrate that DCFL achieves competitive performance on popular federated
learning benchmarks including MNIST, FashionMNIST, SVHN, and CIFAR-10 with
existing FL protocol.
