L
Lifeng Hua
Researcher at Alibaba Group
Publications - 5
Citations - 123
Lifeng Hua is an academic researcher from Alibaba Group. The author has contributed to research in topics: Cloud computing & MNIST database. The author has an hindex of 2, co-authored 4 publications receiving 40 citations.
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Proceedings ArticleDOI
Billion-scale federated learning on mobile clients: a submodel design with tunable privacy
TL;DR: A secure federated submodel learning scheme coupled with a private set union protocol as a cornerstone is designed, which features the properties of randomized response, secure aggregation, and Bloom filter, and endows each client with customized plausible deniability against the position of its desired submodel, thereby protecting private data.
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Secure Federated Submodel Learning.
TL;DR: A secure federated submodel learning scheme coupled with a private set union protocol as a cornerstone is designed, which features the properties of randomized response, secure aggregation, and Bloom filter, and endows each client with a customized plausible deniability against the position of her desired submodel, thus protecting her private data.
Proceedings ArticleDOI
On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption
TL;DR: Wang et al. as mentioned in this paper proposed a new device-cloud collaborative learning framework under the paradigm of domain adaptation, called MPDA, to break the dilemmas of purely cloud-based learning and on-device training.
Proceedings ArticleDOI
Data-Free Evaluation of User Contributions in Federated Learning
TL;DR: Zhang et al. as discussed by the authors proposed a method called Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in FL without a test dataset, which achieves this using the statistical correlation of the model parameters uploaded by users.
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Data-Free Evaluation of User Contributions in Federated Learning
TL;DR: In this article, the authors proposed Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in federated learning without a test dataset, which achieves this using the statistical correlation of the model parameters uploaded by users.