No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

Author:

Liu Ruixuan1,Wu Fangzhao2,Wu Chuhan3,Wang Yanlin2,Lyu Lingjuan4,Chen Hong1,Xie Xing2

Affiliation:

1. Renmin University of China, Beijing, China

2. Microsoft Research Asia, Beijing, China

3. Tsinghua University, Beijing, China

4. Sony AI, Tokyo, Japan

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

ACM

Reference48 articles.

1. Towards federated learning at scale: System design;Bonawitz Keith;Proceedings of Machine Learning and Systems,2019

2. Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning

3. Sebastian Caldas , Jakub Konecny , H Brendan McMahan , and Ameet Talwalkar . 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210 ( 2018 ). Sebastian Caldas, Jakub Konecny, H Brendan McMahan, and Ameet Talwalkar. 2018. Expanding the reach of federated learning by reducing client resource requirements. arXiv preprint arXiv:1812.07210 (2018).

4. Daniel Cera Mona Diabb Eneko Agirrec Inigo Lopez-Gazpioc Lucia Speciad and Basque Country Donostia. 2017. SemEval-2017 Task 1 Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation. (2017). Daniel Cera Mona Diabb Eneko Agirrec Inigo Lopez-Gazpioc Lucia Speciad and Basque Country Donostia. 2017. SemEval-2017 Task 1 Semantic Textual Similarity Multilingual and Cross-lingual Focused Evaluation. (2017).

5. Hongyan Chang , Virat Shejwalkar , Reza Shokri , and Amir Houmansadr . 2019 . Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv preprint arXiv:1912.11279 (2019). Hongyan Chang, Virat Shejwalkar, Reza Shokri, and Amir Houmansadr. 2019. Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer. arXiv preprint arXiv:1912.11279 (2019).

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