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Aggregation Design for Personalized Federated Multi-Modal Learning Over Wireless Networks
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DOI：10.1109/LCOMM.2024.3414451.png)
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Abstract
En 中文
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 non-identically 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.
Keywords:
Performance evaluation
Servers
Feature extraction
Federated learning
Distributed databases
Visualization
Training
multi-Modal learning
aggregation coefficients
Journal
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