  With the increasingly strengthened data privacy act and the difficult data
centralization, Federated Learning (FL) has become an effective solution to
collaboratively train the model while preserving each client's privacy. FedAvg
is a standard aggregation algorithm that makes the proportion of dataset size
of each client as aggregation weight. However, it can't deal with
non-independent and identically distributed (non-i.i.d) data well because of
its fixed aggregation weights and the neglect of data distribution. In this
paper, we propose an aggregation strategy that can effectively deal with
non-i.i.d dataset, namely FedGraph, which can adjust the aggregation weights
adaptively according to the training condition of local models in whole
training process. The FedGraph takes three factors into account from coarse to
fine: the proportion of each local dataset size, the topology factor of model
graphs, and the model weights. We calculate the gravitational force between
local models by transforming the local models into topology graphs. The
FedGraph can explore the internal correlation between local models better
through the weighted combination of the proportion each local dataset, topology
structure, and model weights. The proposed FedGraph has been applied to the
MICCAI Federated Tumor Segmentation Challenge 2021 (FeTS) datasets, and the
validation results show that our method surpasses the previous state-of-the-art
by 2.76 mean Dice Similarity Score. The source code will be available at
Github.
