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Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm, drawing tremendous attention in both academia and industry.
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Leaf: a benchmark for federated settings
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Through a gender lens: learning usage patterns of emojis from large-scale Android users. In Proceedings of the 2018 World Wide Web Conference, WWW 2018 . 763–772
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Federated learning for mobile keyboard prediction
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Ai benchmark: running deep neural networks on android smartphones. In Proceedings of the European Conference on Computer Vision (ECCV) . 288–314
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A generic framework for privacy preserving deep learning
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Deeptype: On-device deep learning for input personalization service with minimal privacy concern
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Applied federated learning: improving google keyboard query suggestions
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signSGD with majority vote is communication efficient and fault tolerant. In Proceedings of 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
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Towards federated learning at scale: system design. In Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019
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A comprehensive study on challenges in deploying deep learning based software. In Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2020 . 750–762
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Deep Learning for Java
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Fedml: a research library and benchmark for federated machine learning
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Device heterogeneity in federated learning: a superquantile approach
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Federated learning: challenges, methods, and future directions
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Federated learning with autotuned communication-efficient secure aggregation
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Towards taming the resource and data heterogeneity in federated learning. In Proceedings of 2019 USENIX Conference on Operational Machine Learning (OpML 19) . 19–21
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Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation
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Neural architecture search: a survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019 · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan. 2019 · 2019
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Advances and open problems in federated learning
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SmartPC: hierarchical pace control in real-time federated learning system. In Proceedings of 2019 IEEE Real-Time Systems Symposium (RTSS) . 406–418
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Exploiting unintended feature leakage in collaborative learning. In Proceedings of 2019 IEEE Symposium on Security and Privacy (SP) . 691–706
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019 · 2019
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Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020a · 2020
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Federated Optimization in Heterogeneous Networks. In Proceedings of Machine Learning and Systems 2020, MLSys 2020
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020b · 2020
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Fair resource allocation in federated learning. In Proceedings of 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2020c · 2020
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Privacy-preserving AI services through data decentralization. In Proceedings of the Web Conference 2020, WWW 2020 . 190–200
Christian Meurisch, Bekir Bayrak, and Max Mühlhäuser. 2020 · 2020
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Large-scale CelebFaces Attributes (CelebA) Dataset
The Chinese University of Hong Kong Multimedia Laboratory. 2020 · 2020
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Federated deep learning in PaddlePaddle
PaddlePaddle. 2020 · 2020
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Reddit Dataset
PushShift.io. 2020 · 2020
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TensorFlow Federated: Machine Learning on Decentralized Data
Tensorflow. 2020 · 2020
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California Consumer Privacy Act
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Compression ratio
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General Data Protection Regulation
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Hierarchical Federated Learning through LAN-WAN Orchestration
Jinliang Yuan, Mengwei Xu, Xiao Ma, Ao Zhou, Xuanzhe Liu, and Shangguang Wang. 2020 · 2020
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An empirical study on deployment faults of deep learning based mobile applications. In Proceedings of the 43rd International Conference on Software Engineering, ICSE 2021 . Accepted to appear
Zhenpeng Chen, Huihan Yao, Yiling Lou, Yanbin Cao, Yuanqiang Liu, Haoyu Wang, and Xuanzhe Liu. 2021 · 2021
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