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Arxiv
4d
218
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Measuring Participant Contributions in Decentralized Federated Learning
- Federated learning (FL) allows multiple clients to train models collaboratively without sharing data.
- Measuring participant contributions in FL is essential for incentivizing clients and ensuring transparency.
- Existing methods for contribution measurement in centralized FL are not directly applicable to decentralized FL.
- Novel methodologies like DFL-Shapley and DFL-MR have been proposed for measuring participant contributions in decentralized federated learning.
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