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Concerned with user data privacy, this paper presents a new federated learning (FL) method that trains machine learning models on edge devices without accessing sensitive data.
“Federated Machine Learning: Concept and Applications” arXiv: 1902.04885
Qiang Yang, Yang Liu, Tianjian Chen and Yongxin Tong · 1902
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“Federated Region-Learning: An Edge Computing Based Framework for Urban Environment Sensing”
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Ying Zhang, Tao Xiang, Timothy. Hospedales and Huchuan Lu · 2018
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“Federated Learning Of Out-Of-Vocabulary Words”
Mingqing Chen, Rajiv Mathews, Tom Ouyang and Françoise Beaufays · 2019
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“Private Federated Learning”
Julien Freudiger · 2019
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“Improving Federated Learning Personalization via Model Agnostic Meta Learning”
Yihan Jiang, Jakub Konečný, Keith Rush and Sreeram Kannan · 2019
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“FedMD: Heterogenous Federated Learning via Model Distillation”
Daliang Li and Junpu Wang · 2019
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“Privacy-preserving Federated Brain Tumour Segmentation”
Wenqi Li et al · 2019
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Dianbo Sui et al · 2020
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“Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization”
Jianyu Wang et al · 2020
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“Federated Accelerated Stochastic Gradient Descent”
Honglin Yuan and Tengyu Ma · 2020
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“Privacy and Artificial Intelligence”
James Curzon, Tracy Kosa, Rajen Akalu and Khalil El-Khatib · 2021
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“Are All Users Treated Fairly in Federated Learning Systems?”
Umberto Michieli and Mete Ozay · 2021
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“Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications”
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Chaoyang He, Murali Annavaram and Salman Avestimehr · 2020
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“SCAFFOLD: Stochastic Controlled Averaging for Federated Learning”
Sai Karimireddy et al · 2020
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“Survey of Personalization Techniques for Federated Learning”
Viraj Kulkarni, Milind Kulkarni and Aniruddha Pant · 2020
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Tian Li et al · 2020
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“On the Convergence of FedAvg on Non-IID Data”
Xiang Li et al · 2020
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“Ensemble Distillation for Robust Model Fusion in Federated Learning”
Tao Lin, Lingjing Kong, Sebastian Stich and Martin Jaggi · 2020
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Matthias Paulik et al · 2021
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“FedKD: Communication Efficient Federated Learning via Knowledge Distillation”
Chuhan Wu et al · 2021
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“Auto graph encoder-decoder for neural network pruning”
Sixing Yu, Arya Mazaheri and Ali Jannesari · 2021
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“HeteroSAg: Secure Aggregation With Heterogeneous Quantization in Federated Learning”
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