Fetching the paper…
Reading the bibliography…
Federated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by maintaining data stored locally.
J. Nocedal, “Updating quasi-newton matrices with limited storage,” Mathematics of Computation , vol. 35, no. 151, pp. 773–782, 1980
1980
Earlier work this paper cites.
A. Hoecker and V. Kartvelishvili, “Svd approach to data unfolding,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , vol. 372, no. 3, pp. 469–481, 1996
1996
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Tech. Rep., 2009
2009
Earlier work this paper cites.
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona, “Caltech-UCSD Birds 200,” California Institute of Technology, Tech. Rep. CNS-TR-2010-001, 2010
2010
Earlier work this paper cites.
C. Dwork, “Differential privacy,” Encyclopedia of Cryptography and Security , pp. 338–340, 2011
2011
Earlier work this paper cites.
J. McAuley and J. Leskovec, “Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text,” in Proceedings of the 7th ACM Conference on Recommender Systems , ser. RecSys ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 165–172
2013
Earlier work this paper cites.
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi, “Fine-grained visual classification of aircraft,” Tech. Rep., 2013
2013
Earlier work this paper cites.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards Making Systems Forget with Machine Unlearning,” in 2015 IEEE Symposium on Security and Privacy , 2015, pp. 463–480
2015
Earlier work this paper cites.
M. P. Naeini, G. Cooper, and M. Hauskrecht, “Obtaining well calibrated probabilities using bayesian binning,” in Proceedings of the AAAI conference on artificial intelligence , vol. 29, no. 1, 2015
2015
Earlier work this paper cites.
Q. Liu and D. Wang, “Stein variational gradient descent: A general purpose bayesian inference algorithm,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 3–18
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in International conference on machine learning . PMLR, 2017, pp. 1885–1894
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
R. Kemker, M. McClure, A. Abitino, T. Hayes, and C. Kanan, “Measuring catastrophic forgetting in neural networks,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in 2018 IEEE 31st computer security foundations symposium (CSF) . IEEE, 2018, pp. 268–282
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Acar, H. Aksu, A. S. Uluagac, and M. Conti, “A Survey on Homomorphic Encryption Schemes: Theory and Implementation,” ACM Comput. Surv. , vol. 51, no. 4, jul 2018
2018
Earlier work this paper cites.
L. Zhu, Z. Liu, and S. Han, “Deep Leakage from Gradients,” in Proc. of Conference on Neural Information Processing Systems , 2019, pp. 14 747–14 756
2019
Earlier work this paper cites.
L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 691–706
2019
Earlier work this paper cites.
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov, “Differential privacy has disparate impact on model accuracy,” in Advances in Neural Information Processing Systems , 2019, pp. 15 453–15 462
2019
Earlier work this paper cites.
S. Truex, N. Baracaldo, A. Anwar, T. Steinke, H. Ludwig, R. Zhang, and Y. Zhou, “A Hybrid Approach to Privacy-preserving Federated Learning,” in Proc. of the ACM Workshop on Artificial Intelligence and Security , 2019, pp. 1–11
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Golatkar, A. Achille, and S. Soatto, “Eternal sunshine of the spotless net: Selective forgetting in deep networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9304–9312
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Lesort, V. Lomonaco, A. Stoian, D. Maltoni, D. Filliat, and N. Díaz-Rodríguez, “Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges,” Information fusion , vol. 58, pp. 52–68, 2020
2020
Earlier work this paper cites.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in International conference on artificial intelligence and statistics . PMLR, 2020, pp. 2938–2948
2020
Earlier work this paper cites.
M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to { \{ Byzantine-Robust } \} federated learning,” in 29th USENIX security symposium (USENIX Security 20) , 2020, pp. 1605–1622
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
J. Geiping, H. Bauermeister, H. Dröge, and M. Moeller, “Inverting Gradients - How easy is it to break privacy in federated learning?” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 16 937–16 947
2020
Cited alongside, same era.
P. Bellavista, L. Foschini, and A. Mora, “Decentralised Learning in Federated Deployment Environments: A System-Level Survey,” ACM Computing Surveys (CSUR) , vol. 54, no. 1, pp. 1–38, 2021
2021
Cited alongside, same era.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 077–12 090, 2021
S. of California Department of Justice, “California consumer privacy act (ccpa),” URL https://oag.ca.gov/privacy/ccpa , accessed on October 2023
2023
Later among the works it cites.
H. Xu, T. Zhu, L. Zhang, W. Zhou, and P. S. Yu, “Machine unlearning: A survey,” ACM Computing Surveys , vol. 56, no. 1, pp. 1–36, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
F. Wang, B. Li, and B. Li, “Federated unlearning and its privacy threats,” IEEE Network , pp. 1–7, 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
A. Golatkar, A. Achille, A. Ravichandran, M. Polito, and S. Soatto, “Mixed-privacy forgetting in deep networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 792–801
2021
Cited alongside, same era.
G. Liu, X. Ma, Y. Yang, C. Wang, and J. Liu, “Federaser: Enabling efficient client-level data removal from federated learning models,” in 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) , 2021, pp. 1–10
2021
Cited alongside, same era.
J. Gong, O. Simeone, and J. Kang, “Bayesian variational federated learning and unlearning in decentralized networks,” in 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) , 2021, pp. 216–220
2021
Cited alongside, same era.
H. Peng, H. Li, Y. Song, V. Zheng, and J. Li, “Differentially Private Federated Knowledge Graphs Embedding,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , ser. CIKM ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 1416–1425. [Online]. Available: https://doi.org/10.1145/3459637.3482252
2021
Cited alongside, same era.
K. Singhal, H. Sidahmed, Z. Garrett, S. Wu, J. Rush, and S. Prakash, “Federated Reconstruction: Partially Local Federated Learning,” in Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., vol. 34. Curran Associates, Inc., 2021, pp. 11 220–11 232
2021
Cited alongside, same era.
D. Byrd and A. Polychroniadou, “Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications,” in Proceedings of the First ACM International Conference on AI in Finance , ser. ICAIF ’20. New York, NY, USA: Association for Computing Machinery, 2021
2021
Cited alongside, same era.
E. Hu, Y. Tang, A. Kyrillidis, and C. Jermaine, “Federated learning over images: Vertical decompositions and pre-trained backbones are difficult to beat,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 385–19 396
2021
Cited alongside, same era.
Z. Yi, X. Wang, I. Ounis, and C. Macdonald, “Multi-modal graph contrastive learning for micro-video recommendation,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 1807–1811
2022
Cited alongside, same era.
2023
Later among the works it cites.
H. Zhang, T. Nakamura, T. Isohara, and K. Sakurai, “A review on machine unlearning,” SN Computer Science , vol. 4, no. 4, p. 337, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
V. S. Chundawat, A. K. Tarun, M. Mandal, and M. Kankanhalli, “Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 6, 2023, pp. 7210–7217
2023
Later among the works it cites.
2023
Later among the works it cites.
N. Su and B. Li, “Asynchronous federated unlearning,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications , 2023, pp. 1–10
2023
Later among the works it cites.
Y. Zhao, P. Wang, H. Qi, J. Huang, Z. Wei, and Q. Zhang, “Federated unlearning with momentum degradation,” IEEE Internet of Things Journal , pp. 1–1, 2023
2023
Later among the works it cites.
W. Yuan, H. Yin, F. Wu, S. Zhang, T. He, and H. Wang, “Federated unlearning for on-device recommendation,” in Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining , 2023, pp. 393–401
2023
Later among the works it cites.
X. Cao, J. Jia, Z. Zhang, and N. Z. Gong, “Fedrecover: Recovering from poisoning attacks in federated learning using historical information,” in 2023 IEEE Symposium on Security and Privacy (SP) , 2023, pp. 1366–1383
2023
Later among the works it cites.
X. Guo, P. Wang, S. Qiu, W. Song, Q. Zhang, X. Wei, and D. Zhou, “FAST: Adopting Federated Unlearning to Eliminating Malicious Terminals at Server Side,” IEEE Transactions on Network Science and Engineering , pp. 1–14, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Gong, O. Simeone, and J. Kang, “Compressed particle-based federated bayesian learning and unlearning,” IEEE Communications Letters , vol. 27, no. 2, pp. 556–560, 2023
2023
Later among the works it cites.
W. Wang, Z. Tian, C. Zhang, A. Liu, and S. Yu, “BFU: Bayesian Federated Unlearning with Parameter Self-Sharing,” in Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security , ser. ASIA CCS ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 567–578
2023
Later among the works it cites.
L. Zhang, T. Zhu, H. Zhang, P. Xiong, and W. Zhou, “FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks,” IEEE Transactions on Information Forensics and Security , vol. 18, pp. 4732–4746, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
P. Wang, Z. Yan, M. S. Obaidat, Z. Yuan, L. Yang, J. Zhang, Z. Wei, and Q. Zhang, “Edge Caching with Federated Unlearning for Low-latency V2X Communications,” IEEE Communications Magazine , pp. 1–7, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Xia, S. Xu, J. Pei, R. Zhang, Z. Yu, W. Zou, L. Wang, and C. Liu, “FedME2: Memory Evaluation & Erase Promoting Federated Unlearning in DTMN,” IEEE Journal on Selected Areas in Communications , vol. 41, no. 11, pp. 3573–3588, 2023
2023
Later among the works it cites.
T. Che, Y. Zhou, Z. Zhang, L. Lyu, J. Liu, D. Yan, D. Dou, and J. Huan, “Fast federated machine unlearning with nonlinear functional theory,” in Proceedings of the 40th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 23–29 Jul 2023, pp. 4241–4268
2023
Later among the works it cites.
X. Zhu, G. Li, and W. Hu, “Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning,” in Proceedings of the ACM Web Conference 2023 , ser. WWW ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 2444–2454
2023
Later among the works it cites.
2023
Later among the works it cites.
TensorFlow, “Tensorflow federated,” https://flower.dev/docs/datasets/d_2 , 2023
2023
Later among the works it cites.
Flower, “Flower datasets,” https://www.tensorflow.org/federated , 2023
2023
Later among the works it cites.
N. Ding, Z. Sun, E. Wei, and R. Berry, “Incentive mechanism design for federated learning and unlearning,” in Proceedings of the Twenty-Fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing , ser. MobiHoc ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 11–20
2023
Later among the works it cites.
Z. Deng, Z. Han, C. Ma, M. Ding, L. Yuan, C. Ge, and Z. Liu, “Vertical federated unlearning on the logistic regression model,” Electronics , vol. 12, no. 14, 2023
2023
Later among the works it cites.
C. Mazzocca, N. Romandini, M. Mendula, R. Montanari, and P. Bellavista, “TruFLaaS: Trustworthy Federated Learning as a Service,” IEEE Internet of Things Journal , vol. 10, no. 24, pp. 21 266–21 281, 2023
2023
Later among the works it cites.