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Enhancing Privacy via Hierarchical Federated Learning
Federated learning suffers from several privacy-related issues that expo...
04/23/2020 ∙ by Aidmar Wainakh, et al. ∙
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Fed+: A Family of Fusion Algorithms for Federated Learning
We present a class of methods for federated learning, which we call Fed+...
09/14/2020 ∙ by Pengqian Yu, et al. ∙
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Scalable Federated Learning over Passive Optical Networks
Two-step aggregation is introduced to facilitate scalable federated lear...
10/29/2020 ∙ by Jun Li, et al. ∙
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FedMD: Heterogenous Federated Learning via Model Distillation
Federated learning enables the creation of a powerful centralized model ...
10/08/2019 ∙ by Daliang Li, et al. ∙
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Overcoming Forgetting in Federated Learning on Non-IID Data
We tackle the problem of Federated Learning in the non i.i.d. case, in w...
10/17/2019 ∙ by Neta Shoham, et al. ∙
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Hierarchically Fair Federated Learning
Federated learning facilitates collaboration among self-interested agent...
04/22/2020 ∙ by Jingfeng Zhang, et al. ∙
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Beyond privacy regulations: an ethical approach to data usage in transportation
With the exponential advancement of business technology in recent years,...
04/01/2020 ∙ by Johannes M. van Hulst, et al. ∙
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A Real-time Contribution Measurement Method for Participants in Federated Learning
09/08/2020 ∙ by Boyi Liu, et al. ∙
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In recent years, individuals, business organizations or the country have paid more and more attention to their data privacy. At the same time, with the rise of federated learning, federated learning is involved in more and more fields. However, there is no good evaluation standard for each agent participating in federated learning. This paper proposes an online evaluation method for federated learning and compares it with the results obtained by Shapley Value in game theory. The method proposed in this paper is more sensitive to data quality and quantity.
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