  Federated Multi-Modal Learning (FMML) is an emerging field that integrates
information from different modalities in federated learning to improve the
learning performance. In this letter, we develop a parameter scheduling scheme
to improve personalized performance and communication efficiency in
personalized FMML, considering the non-independent and nonidentically
distributed (non-IID) data along with the modality heterogeneity. Specifically,
a learning-based approach is utilized to obtain the aggregation coefficients
for parameters of different modalities on distinct devices. Based on the
aggregation coefficients and channel state, a subset of parameters is scheduled
to be uploaded to a server for each modality. Experimental results show that
the proposed algorithm can effectively improve the personalized performance of
FMML.
