  Data scarcity and heterogeneity pose significant performance challenges for
personalized federated learning, and these challenges are mainly reflected in
overfitting and low precision in existing methods. To overcome these
challenges, a multi-layer multi-fusion strategy framework is proposed in this
paper, i.e., the server adopts the network layer parameters of each client
upload model as the basic unit of fusion for information-sharing calculation.
Then, a new fusion strategy combining personalized and generic is purposefully
proposed, and the network layer number fusion threshold of each fusion strategy
is designed according to the network layer function. Under this mechanism, the
L2-Norm negative exponential similarity metric is employed to calculate the
fusion weights of the corresponding feature extraction layer parameters for
each client, thus improving the efficiency of heterogeneous data personalized
collaboration. Meanwhile, the federated global optimal model approximation
fusion strategy is adopted in the network full-connect layer, and this generic
fusion strategy alleviates the overfitting introduced by forceful personalized.
Finally, the experimental results show that the proposed method is superior to
the state-of-the-art methods.
