Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917955625811968 |
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| author | Cai, Yifeng Zhang, Ziqi Li, Ding Guo, Yao Chen, Xiangqun |
| author_facet | Cai, Yifeng Zhang, Ziqi Li, Ding Guo, Yao Chen, Xiangqun |
| contents | Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm that leads to sub-optimal model accuracy and higher training overhead. In this paper, we challenge the prevailing notion of partial-model aggregation and propose a novel "full-weight aggregation" method named Moss, which aggregates all weights within heterogeneous models to preserve comprehensive knowledge. Evaluation across various applications demonstrates that Moss significantly accelerates training, reduces on-device training time and energy consumption, enhances accuracy, and minimizes network bandwidth utilization when compared to state-of-the-art baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10218 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models Cai, Yifeng Zhang, Ziqi Li, Ding Guo, Yao Chen, Xiangqun Machine Learning Cryptography and Security Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm that leads to sub-optimal model accuracy and higher training overhead. In this paper, we challenge the prevailing notion of partial-model aggregation and propose a novel "full-weight aggregation" method named Moss, which aggregates all weights within heterogeneous models to preserve comprehensive knowledge. Evaluation across various applications demonstrates that Moss significantly accelerates training, reduces on-device training time and energy consumption, enhances accuracy, and minimizes network bandwidth utilization when compared to state-of-the-art baselines. |
| title | Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2503.10218 |