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Collaborative In-Context Learning for Indoor Radio Map Estimation
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collaborative learningindoor localizationwireless networks
2610.31702
Builder Relevance
1h ago70%
Abstract
The paper proposes a novel framework for indoor radio map estimation using collaborative in-context learning to overcome challenges associated with supervised learning and privacy-sensitive data.
Reality Card
Core Claim
The Poly-Ripple framework allows multiple clients to collaboratively estimate indoor radio maps without requiring model updates, outperforming existing ICL baselines.
Method / Result
Poly-Ripple consistently outperforms representative ICL baselines in extensive experiments.
Limitations
The method relies on private demonstrations from clients, which may limit reproducibility across different environments.
Paper to code
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