Aggregation Design for Personalized Federated Multi-Modal Learning over Wireless Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916283561279488 |
|---|---|
| author | Yin, Benshun Chen, Zhiyong Tao, Meixia |
| author_facet | Yin, Benshun Chen, Zhiyong Tao, Meixia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aggregation Design for Personalized Federated Multi-Modal Learning over Wireless Networks Yin, Benshun Chen, Zhiyong Tao, Meixia Information Theory Signal Processing 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. |
| title | Aggregation Design for Personalized Federated Multi-Modal Learning over Wireless Networks |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2406.07915 |