2024
DOI: 10.3934/era.2024079
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Research on incentive mechanisms for anti-heterogeneous federated learning based on reputation and contribution

Xiaoyu Jiang,
Ruichun Gu,
Huan Zhan

Abstract: <abstract> <p>An optimization algorithm for federated learning, equipped with an incentive mechanism, is introduced to tackle the challenges of excessive iterations, prolonged training durations, and suboptimal efficiency encountered during model training within the federated learning framework. Initially, the algorithm establishes reputation values that are tied to both time and model loss metrics. This foundation enables the creation of incentive mechanisms aimed at rewarding honest nodes while … Show more

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