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Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence.
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Federated learning: Strategies for improving communication efficiency,
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Auror: defending against poisoning attacks in collaborative deep learning systems,
S. Shen, S. Tople, P. Saxena, · 2016
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Deep learning with differential privacy,
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, L. Zhang, · 2016
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Semi-supervised knowledge transfer for deep learning from private training data,
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, K. Talwar, · 2016
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The EU General Data Protection Regulation (GDPR): European regulation that has a global impact,
M. Goddard, · 2017
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Distributed optimization with arbitrary local solvers,
C. Ma, J. Konečný, M. Jaggi, V. Smith, M. Jordan, P. Richtárik, M. Takáč, · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning,
X. Chen, C. Liu, B. Li, K. Lu, D. Song, · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent,
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, J. Stainer, · 2017
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Deep models under the GAN: information leakage from collaborative deep learning,
B. Hitaj, G. Ateniese, F. Perez-Cruz, · 2017
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Membership inference attacks against machine learning models,
R. Shokri, M. Stronati, C. Song, V. Shmatikov, · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks,
J.-Y. Zhu, T. Park, P. Isola, A. A. Efros, · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data,
B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y Arcas, · 2017
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Machine learning with adversaries: Byzantine tolerant gradient descent,
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, J. Stainer, · 2017
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How to simulate it–a tutorial on the simulation proof technique,
Y. Lindell, · 2017
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Practical secure aggregation for privacy-preserving machine learning,
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, K. Seth, · 2017
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Emnist: Extending mnist to handwritten letters,
G. Cohen, S. Afshar, J. Tapson, A. van Schaik, · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,
H. Xiao, K. Rasul, R. Vollgraf, · 2017
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Deep gradient compression: Reducing the communication bandwidth for distributed training,
Y. Lin, S. Han, H. Mao, Y. Wang, W. J. Dally, · 2017
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Mitigating sybils in federated learning poisoning,
C. Fung, C. J. M. Yoon, I. Beschastnikh, · 2018
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Model poisoning attacks in federated learning,
A. N. Bhagoji, S. Chakraborty, P. Mittal, S. Calo, · 2018
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Stronger data poisoning attacks break data sanitization defenses,
P. W. Koh, J. Steinhardt, P. Liang, · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates,
D. Yin, Y. Chen, R. Kannan, P. Bartlett, · 2018
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The hidden vulnerability of distributed learning in Byzantium,
E. M. El Mhamdi, R. Guerraoui, S. Rouault, · 2018
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Chained anomaly detection models for federated learning: An intrusion detection case study,
D. Preuveneers, V. Rimmer, I. Tsingenopoulos, J. Spooren, W. Joosen, E. Ilie-Zudor, · 2018
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Learning differentially private recurrent language models,
B. McMahan, D. Ramage, K. Talwar, L. Zhang, · 2018
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signSGD: Compressed optimisation for non-convex problems,
J. Bernstein, Y.-X. Wang, K. Azizzadenesheli, A. Anandkumar, · 2018
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Scalable private learning with PATE,
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, Ú. Erlingsson, · 2018
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Protection against reconstruction and its applications in private federated learning,
A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, R. Rogers, · 2018
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Economic issues in bitcoin mining and blockchain research,
R. Qin, Y. Yuan, S. Wang, F.-Y. Wang, · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting,
S. Yeom, I. Giacomelli, M. Fredrikson, S. Jha, · 2018
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Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, H. Yu, Federated Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, 2019
2019
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Analyzing federated learning through an adversarial lens,
A. Bhagoji, S. Chakraborty, P. Mittal, S. Calo, · 2019
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Federated machine learning: Concept and applications,
Q. Yang, Y. Liu, T. Chen, Y. Tong, · 2019
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Demystifying membership inference attacks in machine learning as a service,
S. Truex, L. Liu, M. E. Gursoy, L. Yu, W. Wei, · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,
M. Nasr, R. Shokri, A. Houmansadr, · 2019
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Can you really backdoor federated learning?,
Z. Sun, P. Kairouz, A. T. Suresh, H. B. McMahan, · 2019
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Analyzing federated learning through an adversarial lens,
A. N. Bhagoji, S. Chakraborty, P. Mittal, S. Calo, · 2019
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Understanding distributed poisoning attack in federated learning,
D. Cao, S. Chang, Z. Lin, G. Liu, D. Sun, · 2019
Cited alongside, same era.
Poisoning attack in federated learning using generative adversarial nets,
J. Zhang, J. Chen, D. Wu, B. Chen, S. Yu, · 2019
Cited alongside, same era.
Quantification of the leakage in federated learning,
Z. Li, Z. Huang, C. Chen, C. Hong, · 2019
Cited alongside, same era.
Beyond inferring class representatives: User-level privacy leakage from federated learning,
Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, H. Qi, · 2019
Cited alongside, same era.
A Novel User Membership Leakage Attack in Collaborative Deep Learning,
Y. Mao, X. Zhu, W. Zheng, D. Yuan, J. Ma, · 2019
Cited alongside, same era.
Eavesdrop the Composition Proportion of Training Labels in Federated Learning,
Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning,
S. Kadhe, N. Rajaraman, O. O. Koyluoglu, K. Ramchandran, · 2020
Later among the works it cites.
Fedopt: towards communication efficiency and privacy preservation in federated learning,
M. Asad, A. Moustafa, T. Ito, · 2020
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Secure federated averaging algorithm with differential privacy,
Y. Li, T.-H. Chang, C.-Y. Chi, · 2020
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C. Sabater, A. Bellet, J. Ramon, · 2020
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A survey on the security of blockchain systems,
X. Li, P. Jiang, T. Chen, X. Luo, Q. Wen, · 2020
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L. Wang, S. Xu, X. Wang, Q. Zhu, · 2019
Cited alongside, same era.
Exploiting unintended feature leakage in collaborative learning,
L. Melis, C. Song, E. De Cristofaro, V. Shmatikov, · 2019
Cited alongside, same era.
Robust aggregation for federated learning,
K. Pillutla, S. M. Kakade, Z. Harchaoui, · 2019
Cited alongside, same era.
Byzantine-robust federated machine learning through adaptive model averaging,
L. Muñoz-González, K. T. Co, E. C. Lupu, · 2019
Cited alongside, same era.
Dïot: A federated self-learning anomaly detection system for iot,
T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, A.-R. Sadeghi, · 2019
Cited alongside, same era.
Pdgan: A novel poisoning defense method in federated learning using generative adversarial network,
Y. Zhao, J. Chen, J. Zhang, D. Wu, J. Teng, S. Yu, · 2019
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy,
E. Bagdasaryan, O. Poursaeed, V. Shmatikov, · 2019
Cited alongside, same era.
Mitigations on sybil-based double-spend attacks in bitcoin,
S. Zhang, J.-H. Lee, · 2020
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Multi-objective evolutionary federated learning,
H. Zhu, Y. Jin, · 2020
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S. Al-Kuwari, Multiple Perspectives on Artificial Intelligence in Healthcare: Opportunities and Challenges, Springer International Publishing, 2021, pp. 65–77
2021
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F. Boissay, T. Ehlers, L. Gambacorta, H. S. Shin, The Palgrave Handbook of Technological Finance, Springer International Publishing, 2021, pp. 855–875
2021
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Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things,
J. Zhang, D. Tao, · 2021
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Leakage of dataset properties in Multi-Party machine learning,
W. Zhang, S. Tople, O. Ohrimenko, · 2021
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Sageflow: Robust federated learning against both stragglers and adversaries,
J. Park, D.-J. Han, M. Choi, J. Moon, · 2021
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Defending against backdoors in federated learning with robust learning rate,
M. S. Ozdayi, M. Kantarcioglu, Y. R. Gel, · 2021
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Free-rider attacks on model aggregation in federated learning,
Y. Fraboni, R. Vidal, M. Lorenzi, · 2021
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An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies,
D. Enthoven, Z. Al-Ars, · 2021
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A survey on security and privacy of federated learning,
V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, G. Srivastava, · 2021
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A Taxonomy of Attacks on Federated Learning,
M. S. Jere, T. Farnan, F. Koushanfar, · 2021
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Vulnerabilities in Federated Learning,
N. Bouacida, P. Mohapatra, · 2021
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Data poisoning attacks on federated machine learning,
G. Sun, Y. Cong, J. Dong, Q. Wang, L. Lyu, J. Liu, · 2021
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A. Salem, R. Wen, M. Backes, S. Ma, Y. Zhang, Dynamic backdoor attacks against deep neural networks, 2021
2021
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Challenges and approaches for mitigating byzantine attacks in federated learning,
S. Hu, J. Lu, W. Wan, L. Y. Zhang, · 2021
Later among the works it cites.
Free-rider attacks on model aggregation in federated learning,
Y. Fraboni, R. Vidal, M. Lorenzi, · 2021
Later among the works it cites.
Lomar: A local defense against poisoning attack on federated learning,
X. Li, Z. Qu, S. Zhao, B. Tang, Z. Lu, Y. Liu, · 2021
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Poisongan: Generative poisoning attacks against federated learning in edge computing systems,
J. Zhang, B. Chen, X. Cheng, H. T. T. Binh, S. Yu, · 2021
Later among the works it cites.
Exploring the limits of out-of-distribution detection,
S. Fort, J. Ren, B. Lakshminarayanan, · 2021
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Covert channel attack to federated learning systems,
G. Costa, F. Pinelli, S. Soderi, G. Tolomei, · 2021
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Baffle: Backdoor detection via feedback-based federated learning,
S. Andreina, G. A. Marson, H. Möllering, G. O. Karame, · 2021
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GRNN: Generative Regression Neural Network – A Data Leakage Attack for Federated Learning,
H. Ren, J. Deng, X. Xie, · 2021
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CAFE: Catastrophic Data Leakage in Vertical Federated Learning,
X. Jin, R. Du, P.-Y. Chen, T. Chen, · 2021
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Beyond Class-Level Privacy Leakage: Breaking Record-Level Privacy in Federated Learning,
X. Yuan, X. Ma, L. Zhang, Y. Fang, D. Wu, · 2021
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Feature inference attack on model predictions in vertical federated learning,
X. Luo, Y. Wu, X. Xiao, B. C. Ooi, · 2021
Later among the works it cites.
Label Leakage and Protection in Two-party Split Learning,
O. Li, J. Sun, X. Yang, W. Gao, H. Zhang, J. Xie, V. Smith, C. Wang, · 2021
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A survey on multi-task learning,
Y. Zhang, Q. Yang, · 2021
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Property Inference From Poisoning,
M. Chase, E. Ghosh, S. Mahloujifar, · 2021
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Exploiting Unintended Property Leakage in Blockchain-Assisted Federated Learning for Intelligent Edge Computing,
M. Shen, H. Wang, B. Zhang, L. Zhu, K. Xu, Q. Li, X. Du, · 2021
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Attack-resistant federated learning with residual-based reweighting,
S. Fu, C. Xie, B. Li, Q. Chen, · 2021
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A game-theoretic approach for robust federated learning,
E. Tahanian, M. Amouei, H. Fateh, M. Rezvani, · 2021
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Advances and open problems in federated learning,
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. B. Charles, G. Cormode, R. Cummings, R. G. L. D’Oliveira, S. Y. E. Rouayheb, D. Evans, J. Gardner, Z. Garrett, A. Gascón, B. Ghazi, P. B. Gibbons, M. Gruteser, Z. Harchaoui, C. He, L. He, Z. Huo, B. Hutchinson, J. Hsu, M. Jaggi, T. Javidi, G. Joshi, M. Khodak, J. Konecný, A. Korolova, F. Koushanfar, O. Koyejo, T. Lepoint, Y. Liu, P. Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, M. Raykova, H. Qi, D. Ramage, R. Raskar, D. X. Song, W. Song, S. U. Stich, Z. Sun, A. T. Suresh, F. Tramèr, P. Vepakomma, J. Wang, L. Xiong, Z. Xu, Q. Yang, F. X. Yu, H. Yu, S. Zhao, · 2021
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Towards federated learning with byzantine-robust client weighting,
A. Portnoy, Y. Tirosh, D. Hendler, · 2021
Later among the works it cites.
Federated f-differential privacy,
Q. Zheng, S. Chen, Q. Long, W. Su, · 2021
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Data poisoning attacks to local differential privacy protocols,
X. Cao, J. Jia, N. Z. Gong, · 2021
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Gradient-leakage resilient federated learning,
W. Wei, L. Liu, Y. Wut, G. Su, A. Iyengar, · 2021
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Digestive Neural Networks: A Novel Defense Strategy Against Inference Attacks in Federated Learning,
H. Lee, J. Kim, S. Ahn, R. Hussain, S. Cho, J. Son, · 2021
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Matrix Sketching for Secure Collaborative Machine Learning,
M. Zhang, S. Wang, · 2021
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An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning,
X. Yang, Y. Feng, W. Fang, J. Shao, X. Tang, S.-T. Xia, R. Lu, · 2021
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Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption,
J. Ma, S.-A. Naas, S. Sigg, X. Lyu, · 2021
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Secure Neural Network in Federated Learning with Model Aggregation under Multiple Keys,
Z. L. Jiang, H. Guo, Y. Pan, Y. Liu, X. Wang, J. Zhang, · 2021
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SAFE: Secure Aggregation with Failover and Encryption,
T. Sandholm, S. Mukherjee, B. A. Huberman, · 2021
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Privacy-preserving federated learning framework based on chained secure multiparty computing,
Y. Li, Y. Zhou, A. Jolfaei, D. Yu, G. Xu, X. Zheng, · 2021
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Fedxgboost: Privacy-preserving xgboost for federated learning,
N. K. Le, Y. Liu, Q. M. Nguyen, Q. Liu, F. Liu, Q. Cai, S. Hirche, · 2021
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The distributed discrete gaussian mechanism for federated learning with secure aggregation,
P. Kairouz, Z. Liu, T. Steinke, · 2021
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Federated learning meets blockchain in edge computing: Opportunities and challenges,
D. C. Nguyen, M. Ding, Q.-V. Pham, P. N. Pathirana, L. B. Le, A. Seneviratne, J. Li, D. Niyato, H. V. Poor, · 2021
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Efficientnetv2: Smaller models and faster training,
M. Tan, Q. Le, · 2021
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Levit: a vision transformer in convnet’s clothing for faster inference,
B. Graham, A. El-Nouby, H. Touvron, P. Stock, A. Joulin, H. J’egou, M. Douze, · 2021
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Homophily outlier detection in non-iid categorical data,
G. Pang, L. Cao, L. Chen, · 2021
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Towards personalized federated learning,
A. Z. Tan, H. Yu, L. Cui, Q. Yang, · 2021
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Fairfed: Enabling group fairness in federated learning,
Y. H. Ezzeldin, S. Yan, C. He, E. Ferrara, S. Avestimehr, · 2021
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Back to the drawing board: A critical evaluation of poisoning attacks on federated learning,
V. Shejwalkar, A. Houmansadr, P. Kairouz, D. Ramage, · 2022
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