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arXiv · 2503.10218

Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models

Yifeng Cai·Ziqi Zhang·Ding Li·Yao Guo·Xiangqun Chen
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Abstract

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.

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Yifeng Cai, Ziqi Zhang, Ding Li, Yao Guo, Xiangqun Chen. 2025-03-13. Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models. https://doi.org/10.1145/3712274

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