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Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants while keeping their data in local clients.
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Local SGD Converges Fast and Communicates Little. In International Conference on Learning Representations
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Applied federated learning: Improving google keyboard query suggestions
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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. In International Conference on Machine Learning . 5650–5659
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Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning . PMLR, 634–643
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019 · 2019
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Federated Learning of N-Gram Language Models. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) . 121–130
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. 2019 · 2019
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Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 5693–5700
Hao Yu, Sen Yang, and Shenghuo Zhu. 2019 · 2019
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Communication-efficient distributed blockwise momentum SGD with error-feedback. In Advances in Neural Information Processing Systems . 11450–11460
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Local model poisoning attacks to byzantine-robust federated learning. In 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) . 1605–1622
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020 · 2020
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Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework
Dhruv Guliani, Francoise Beaufays, and Giovanni Motta. 2020 · 2020
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Nikola Konstantinov and Christoph Lampert. 2019 · 2019
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Fair Resource Allocation in Federated Learning. In International Conference on Learning Representations
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith. 2019 · 2019
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Gaussian Process Learning for Distributed Sensor Networks under False Data Injection Attacks. In 2019 IEEE Conference on Dependable and Secure Computing (DSC) . IEEE, 1–6
Xiuming Liu and Edith Ngai. 2019 · 2019
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Real-world image datasets for federated learning
Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, and Qiang Yang. 2019 · 2019
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Data Poisoning Attacks in Multi-Party Learning. In International Conference on Machine Learning . 4274–4283
Saeed Mahloujifar, Mohammad Mahmoody, and Ameer Mohammed. 2019 · 2019
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Agnostic Federated Learning. In International Conference on Machine Learning . 4615–4625
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. 2019 · 2019
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Federated Adversarial Domain Adaptation. In International Conference on Learning Representations
Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko. 2019 · 2019
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Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. 2019 · 2019
Cited alongside, same era.
FedBoost: A Communication-Efficient Algorithm for Federated Learning. In International Conference on Machine Learning . PMLR, 3973–3983
Jenny Hamer, Mehryar Mohri, and Ananda Theertha Suresh. 2020 · 2020
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Robust Federated Learning via Collaborative Machine Teaching.. In AAAI . 4075–4082
Yufei Han and Xiangliang Zhang. 2020 · 2020
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Learning to Detect Malicious Clients for Robust Federated Learning
Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. 2020a · 2020
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Communication-Efficient Collaborative Learning of Geo-Distributed JointCloud from Heterogeneous Datasets. In 2020 IEEE International Conference on Joint Cloud Computing . IEEE, 22–29
Xiaoli Li, Nan Liu, Chuan Chen, Zibin Zheng, Huizhong Li, and Qiang Yan. 2020b · 2020
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Ensemble Distillation for Robust Model Fusion in Federated Learning. In NeurIPS
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020 · 2020
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Threats to federated learning: A survey
Lingjuan Lyu, Han Yu, and Qiang Yang. 2020 · 2020
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Dynamic Federated Learning Model for Identifying Adversarial Clients
Nuria Rodríguez-Barroso, Eugenio Martínez-Cámara, M Luzón, Gerardo González Seco, Miguel Ángel Veganzones, and Francisco Herrera. 2020 · 2020
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On the Byzantine Robustness of Clustered Federated Learning. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 8861–8865
Felix Sattler, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek. 2020 · 2020
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Byzantine-resilient secure federated learning
Jinhyun So, Başak Güler, and A Salman Avestimehr. 2020 · 2020
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FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 2118–2128
Dianbo Sui, Yubo Chen, Jun Zhao, Yantao Jia, Yuantao Xie, and Weijian Sun. 2020 · 2020
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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. In ISOC Network and Distributed System Security Symposium (NDSS)
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong. 2021 · 2021
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The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). 5201–5212
Peter Kairouz, Ziyu Liu, and Thomas Steinke. 2021 · 2021
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Ditto: Fair and robust federated learning through personalization. In International Conference on Machine Learning
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith. 2021 · 2021
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