Controlling update distance and enhancing fair trainable prototypes in federated learning under data and model heterogeneity
Heterogeneous federated learning (HtFL) has gained significant attention due to its ability to accommodate diverse models and data from distributed combat units. The prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model parameters. These methods allow...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-05-01
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| Series: | Defence Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214914724002988 |
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