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The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI).
V. Klema and A. Laub, “The singular value decomposition: Its computation and some applications,” IEEE Transactions on automatic control , vol. 25, no. 2, pp. 164–176, 1980
1980
Earlier work this paper cites.
J. R. Friedman, V. Patel, W. Chen, S. Tolpygo, and J. E. Lukens, “Quantum superposition of distinct macroscopic states,” nature , vol. 406, no. 6791, pp. 43–46, 2000
2000
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
R. Horodecki, P. Horodecki, M. Horodecki, and K. Horodecki, “Quantum entanglement,” Reviews of modern physics , vol. 81, no. 2, p. 865, 2009
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 22, no. 10, pp. 1345–1359, 2010
2010
Earlier work this paper cites.
O. Romero-Isart, A. C. Pflanzer, F. Blaser, R. Kaltenbaek, N. Kiesel, M. Aspelmeyer, and J. I. Cirac, “Large quantum superpositions and interference of massive nanometer-sized objects,” Physical review letters , vol. 107, no. 2, p. 020405, 2011
2011
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012 . icml.cc / Omnipress, 2012
2012
Earlier work this paper cites.
S. Barz, E. Kashefi, A. Broadbent, J. F. Fitzsimons, A. Zeilinger, and P. Walther, “Demonstration of blind quantum computing,” science , vol. 335, no. 6066, pp. 303–308, 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature communications , vol. 5, no. 1, p. 4213, 2014
2014
Earlier work this paper cites.
2016
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.
2017
Earlier work this paper cites.
P. Blanchard, E. M. E. Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 119–129
2017
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.
Z. Hou, H. Chen, Y. Li, and B. Vucetic, “Incentive mechanism design for wireless energy harvesting-based internet of things,” IEEE Internet of Things Journal , vol. 5, no. 4, pp. 2620–2632, 2017
2017
Earlier work this paper cites.
T. Liu, J. Li, F. Shu, M. Tao, W. Chen, and Z. Han, “Design of contract-based trading mechanism for a small-cell caching system,” IEEE Transactions on Wireless Communications , vol. 16, no. 10, pp. 6602–6617, 2017
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.
A. Khan, G. Saha, and R. K. Pal, “Quantum computing based inference of grns,” in Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part II 5 . Springer, 2017, pp. 221–233
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding with unsupervised learning,” Open AI , 2018
2018
Earlier work this paper cites.
M. Fang, G. Yang, N. Z. Gong, and J. Liu, “Poisoning attacks to graph-based recommender systems,” in Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018 . ACM, 2018, pp. 381–392
2018
Earlier work this paper cites.
M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li, “Manipulating machine learning: Poisoning attacks and countermeasures for regression learning,” in 2018 IEEE Symposium on Security and Privacy, SP 2018, Proceedings, 21-23 May 2018, San Francisco, California, USA . IEEE Computer Society, 2018, pp. 19–35
2018
Earlier work this paper cites.
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein, “Poison frogs! targeted clean-label poisoning attacks on neural networks,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada , 2018, pp. 6106–6116
2018
Earlier work this paper cites.
D. Yin, Y. Chen, K. Ramchandran, and P. L. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 5636–5645
2018
Earlier work this paper cites.
T. Bahreini, H. Badri, and D. Grosu, “An envy-free auction mechanism for resource allocation in edge computing systems,” in Proceedings of the 2018 IEEE/ACM Symposium on Edge Computing (SEC’18) , 2018, pp. 313–322
2018
Earlier work this paper cites.
Y. Jiao, P. Wang, D. Niyato, and Z. Xiong, “Social welfare maximization auction in edge computing resource allocation for mobile blockchain,” in Proceedings of the 2018 IEEE International Conference on Communications (ICC’18) , 2018, pp. 1–6
2018
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
Earlier work this paper cites.
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in 2019 IEEE Symposium on Security and Privacy, SP 2019, San Francisco, CA, USA, May 19-23, 2019 . IEEE, 2019, pp. 707–723
2019
Earlier work this paper cites.
C. Xie, S. Koyejo, and I. Gupta, “Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance,” in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA , ser. Proceedings of Machine Learning Research, vol. 97. PMLR, 2019, pp. 6893–6901
2019
Earlier work this paper cites.
L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019, pp. 14 747–14 756
2019
Earlier work this paper cites.
J. Kang, Z. Xiong, S. Niyato, and J. Zhang, “Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory,” IEEE Internet of Things Journal , vol. 6, no. 6, pp. 10 700–10 714, 2019
2019
Earlier work this paper cites.
J. Kang, Z. Xiong, D. Niyato, D. Ye, D. I. Kim, and J. Zhao, “Toward secure blockchain-enabled internet of vehicles: Optimizing consensus management using reputation and contract theory,” IEEE Transactions on Vehicular Technology , vol. 68, no. 3, pp. 2906–2920, 2019
2019
Earlier work this paper cites.
Y. Sarikaya and O. Ercetin, “Motivating workers in federated learning: A stackelberg game perspective,” IEEE Networking Letters , vol. 2, no. 1, pp. 23–27, 2019
2019
Earlier work this paper cites.
S. Feng, D. Niyato, P. Wang, D. I. Kim, and Y.-C. Liang, “Joint service pricing and cooperative relay communication for federated learning,” in 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) . IEEE, 2019, pp. 815–820
2019
Earlier work this paper cites.
T. Song, Y. Tong, and S. Wei, “Profit allocation for federated learning,” in IEEE BigData , 2019, pp. 2577–2586
2019
Earlier work this paper cites.
Z. Li, Z. Yang, S. Xie, W. Chen, and K. Liu, “Credit-based payments for fast computing resource trading in edge-assisted internet of things,” IEEE Internet of Things Journal , vol. 6, no. 4, pp. 6606–6617, 2019
2019
Earlier work this paper cites.
A. Zavodovski, S. Bayhan, N. Mohan, P. Zhou, W. Wong, and J. Kangasharju, “Decloud: Truthful decentralized double auction for edge clouds,” in Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS’19) , 2019, pp. 2157–2167
2019
Earlier work this paper cites.
G. Gao, M. Xiao, J. Wu, H. Huang, S. Wang, and G. Chen, “Auction-based vm allocation for deadline-sensitive tasks in distributed edge cloud,” IEEE Transactions on Services Computing , vol. 14, no. 6, pp. 1702–1716, 2019
2019
Earlier work this paper cites.
Y. Jiao, P. Wang, D. Niyato, and K. Suankaewmanee, “Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks,” IEEE Transactions on Parallel and Distributed Systems , vol. 30, no. 9, pp. 1975–1989, 2019
2019
Earlier work this paper cites.
G. Wang, C. X. Dang, and Z. Zhou, “Measure contribution of participants in federated learning,” in IEEE Big Data , 2019, pp. 2597–2604
2019
Earlier work this paper cites.
P. W. W. Koh, K.-S. Ang, H. Teo, and P. S. Liang, “On the accuracy of influence functions for measuring group effects,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
G. Wang, “Interpret federated learning with shapley values,” arXiv preprint arXiv:1905.04519 , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning,” Ieee Network , vol. 33, no. 5, pp. 156–165, 2019
2019
Earlier work this paper cites.
N. H. Tran, W. Bao, A. Zomaya, M. N. Nguyen, and C. S. Hong, “Federated learning over wireless networks: Optimization model design and analysis,” in IEEE INFOCOM 2019-IEEE conference on computer communications . IEEE, 2019, pp. 1387–1395
2019
Earlier work this paper cites.
T. Huang, W. Yang, J. Wu, J. Ma, X. Zhang, and D. Zhang, “A survey on green 6g network: Architecture and technologies,” IEEE access , vol. 7, pp. 175 758–175 768, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine learning and systems , vol. 2, pp. 429–450, 2020
2020
Earlier work this paper cites.
Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, and H. Yu, Federated Learning . Springer, Cham, 2020, vol. Synthesis Lectures on Artificial Intelligence and Machine Learning
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
G. Long, Y. Tan, J. Jiang, and C. Zhang, “Federated learning for open banking,” in Federated Learning: Privacy and Incentive . Springer, 2020, pp. 240–254
2020
Earlier work this paper cites.
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy] , ser. Proceedings of Machine Learning Research, vol. 108. PMLR, 2020, pp. 2938–2948
2020
Earlier work this paper cites.
——, “Zeno++: Robust fully asynchronous SGD,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 10 495–10 503
2020
Earlier work this paper cites.
M. Fang, X. Cao, J. Jia, and N. Z. Gong, “Local model poisoning attacks to byzantine-robust federated learning,” in 29th USENIX Security Symposium, USENIX Security 2020, August 12-14, 2020 . USENIX Association, 2020, pp. 1605–1622
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
N. Ding, Z. Fang, and J. Huang, “Incentive mechanism design for federated learning with multi-dimensional private information,” in 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT) . IEEE, 2020, pp. 1–8
2020
Earlier work this paper cites.
S. R. Pandey, N. H. Tran, and C. S. Hong, “A crowdsourcing framework for on-device federated learning,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3241–3256, 2020
2020
Earlier work this paper cites.
C. T. Dinh, N. H. Tran, M. N. Nguyen, C. S. Hong, W. Bao, A. Y. Zomaya, and V. Gramoli, “Federated learning over wireless networks: Convergence analysis and resource allocation,” IEEE/ACM Transadinh2020federatedctions on Networking , vol. 29, no. 1, pp. 398–409, 2020
2020
Earlier work this paper cites.
L. U. Khan, S. R. Pandey, N. H. Tran, W. Saad, Z. Han, M. N. Nguyen, and C. S. Hong, “Federated learning for edge networks: Resource optimization and incentive mechanism,” IEEE Communications Magazine , vol. 58, no. 10, pp. 88–93, 2020
2020
Earlier work this paper cites.
R. Hu and Y. Gong, “Trading data for learning: Incentive mechanism for on-device federated learning,” in GLOBECOM 2020-2020 IEEE Global Communications Conference . IEEE, 2020, pp. 1–6
2020
Earlier work this paper cites.
J. Lee, D. Kim, and D. Niyato, “Market analysis of distributed learning resource management for internet of things: A game-theoretic approach,” IEEE Internet of Things Journal , vol. 7, no. 9, pp. 8430–8439, 2020
2020
Earlier work this paper cites.
Y. Sarikaya and O. Ercetin, “Regulating workers in federated learning by yardstick competition,” in Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools , 2020, pp. 150–155
2020
Earlier work this paper cites.
X. Qu, Q. Hu, and S. Wang, “Privacy-preserving model training architecture for intelligent edge computing,” Computer Communications , vol. 162, pp. 94–101, 2020
2020
Earlier work this paper cites.
H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, and Q. Yang, “A sustainable incentive scheme for federated learning,” IEEE Intelligent Systems , vol. 35, no. 4, pp. 58–69, 2020
2020
Earlier work this paper cites.
H.-J. Hong, W. Fan, C. E. Chow, X. Zhou, and S.-Y. Chang, “Optimizing social welfare for task offloading in mobile edge computing,” in Proceedings of the 2020 IFIP Networking Conference (Networking’20) , 2020, pp. 524–528
2020
Earlier work this paper cites.
S. Yang, “A task offloading solution for internet of vehicles using combination auction matching model based on mobile edge computing,” IEEE Access , vol. 8, pp. 53 261–53 273, 2020
2020
Earlier work this paper cites.
Y. Jiao, P. Wang, D. Niyato, B. Lin, and D. I. Kim, “Toward an automated auction framework for wireless federated learning services market,” IEEE Transactions on Mobile Computing , vol. 20, no. 10, pp. 3034–3048, 2020
2020
Earlier work this paper cites.
R. Zeng, S. Zhang, J. Wang, and X. Chu, “Fmore: An incentive scheme of multi-dimensional auction for federated learning in MEC,” in ICDCS , 2020, pp. 278–288
2020
Earlier work this paper cites.
C. Ying, H. Jin, X. Wang, and Y. Luo, “Double insurance: Incentivized federated learning with differential privacy in mobile crowdsensing,” in 2020 International Symposium on Reliable Distributed Systems (SRDS) . IEEE, 2020, pp. 81–90
2020
Earlier work this paper cites.
T. H. T. Le, N. H. Tran, Y. K. Tun, Z. Han, and C. S. Hong, “Auction based incentive design for efficient federated learning in cellular wireless networks,” in WCNC , 2020, pp. 1–6
2020
Earlier work this paper cites.
Y. Liu, Z. Ai, S. Sun, S. Zhang, Z. Liu, and H. Yu, “Fedcoin: A peer-to-peer payment system for federated learning,” in Federated learning: privacy and incentive . Springer, 2020, pp. 125–138
2020
Earlier work this paper cites.
A. Ghosh, J. Chung, D. Yin, and K. Ramchandran, “An efficient framework for clustered federated learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 19 586–19 597, 2020
2020
Earlier work this paper cites.
S. Wei, Y. Tong, Z. Zhou, and T. Song, “Efficient and fair data valuation for horizontal federated learning,” in Federated Learning . Springer, 2020, pp. 139–152
2020
Earlier work this paper cites.
T. Wang, J. Rausch, C. Zhang, R. Jia, and D. Song, “A principled approach to data valuation for federated learning,” in Federated Learning . Springer, 2020, pp. 153–167
2020
Earlier work this paper cites.
F. Pan, D. Meng, Y. Zhang, and X. Li, “Secure federated feature selection for cross-feature federated learning,” arXiv preprint , 2020
2020
Earlier work this paper cites.
Y. Chen, Y. Ning, Z. Chai, and H. Rangwala, “Federated multi-task learning with hierarchical attention for sensor data analytics,” in 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2020, pp. 1–8
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C. He, M. Annavaram, and S. Avestimehr, “Group knowledge transfer: Federated learning of large CNNs at the edge,” Advances in Neural Information Processing Systems , vol. 33, pp. 14 068–14 080, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, “Federated learning: A survey on enabling technologies, protocols, and applications,” IEEE Access , vol. 8, pp. 140 699–140 725, 2020
2020
Earlier work this paper cites.
Y. Liu, J. Peng, J. Kang, A. M. Iliyasu, D. Niyato, and A. A. Abd El-Latif, “A secure federated learning framework for 5g networks,” IEEE Wireless Communications , vol. 27, no. 4, pp. 24–31, 2020
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.
J. Bausch, “Recurrent quantum neural networks,” Advances in neural information processing systems , vol. 33, pp. 1368–1379, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al. , “Advances and open problems in federated learning,” Foundations and trends® in machine learning , vol. 14, no. 1–2, pp. 1–210, 2021
2021
Earlier work this paper cites.
S. Horvath, S. Laskaridis, M. Almeida, I. Leontiadis, S. Venieris, and N. Lane, “Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 876–12 889, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning . Pmlr, 2021, pp. 8821–8831
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
B. Guo, Y. Mei, D. Xiao, and W. Wu, “Pfl-moe: Personalized federated learning based on mixture of experts,” in Proc. APWeb-WAIM , vol. 12858. Guangzhou, China: Springer, 2021, pp. 480–486
2021
Cited alongside, same era.
2021
Cited alongside, same era.
W. Yuan, G. Neubig, and P. Liu, “Bartscore: Evaluating generated text as text generation,” Advances in Neural Information Processing Systems , vol. 34, pp. 27 263–27 277, 2021
2021
Cited alongside, same era.
X. Lyu, Y. Han, W. Wang, J. Liu, B. Wang, J. Liu, and X. Zhang, “Poisoning with cerberus: Stealthy and colluded backdoor attack against federated learning,” in Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023 . AAAI Press, 2023, pp. 9020–9028
2023
Later among the works it cites.
K. Mei, Z. Li, Z. Wang, Y. Zhang, and S. Ma, “NOTABLE: transferable backdoor attacks against prompt-based NLP models,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 . Association for Computational Linguistics, 2023, pp. 15 551–15 565
2023
Later among the works it cites.
S. Zhao, J. Wen, A. T. Luu, J. Zhao, and J. Fu, “Prompt as triggers for backdoor attack: Examining the vulnerability in language models,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 . Association for Computational Linguistics, 2023, pp. 12 303–12 317
2023
Later among the works it cites.
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2021
Cited alongside, same era.
M. Hao, H. Li, G. Xu, H. Chen, and T. Zhang, “Efficient, private and robust federated learning,” in ACSAC ’21: Annual Computer Security Applications Conference, Virtual Event, USA, December 6 - 10, 2021 . ACM, 2021, pp. 45–60
2021
Cited alongside, same era.
X. Cao, M. Fang, J. Liu, and N. Z. Gong, “Fltrust: Byzantine-robust federated learning via trust bootstrapping,” in 28th Annual Network and Distributed System Security Symposium, NDSS 2021, virtually, February 21-25, 2021 . The Internet Society, 2021
2021
Cited alongside, same era.
P. Sun, H. Che, Z. Wang, Y. Wang, T. Wang, L. Wu, and H. Shao, “Pain-fl: Personalized privacy-preserving incentive for federated learning,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 12, pp. 3805–3820, 2021
2021
Cited alongside, same era.
B. Wang, F. Wu, Y. Long, L. Rimanic, C. Zhang, and B. Li, “Datalens: Scalable privacy preserving training via gradient compression and aggregation,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security , 2021, pp. 2146–2168
2021
Cited alongside, same era.
B. G. Tekgul, Y. Xia, S. Marchal, and N. Asokan, “Waffle: Watermarking in federated learning,” in 2021 40th International Symposium on Reliable Distributed Systems (SRDS) . IEEE, 2021, pp. 310–320
2021
Cited alongside, same era.
W. Y. B. Lim, J. Huang, Z. Xiong, J. Kang, D. Niyato, X.-S. Hua, C. Leung, and C. Miao, “Towards federated learning in uav-enabled internet of vehicles: A multi-dimensional contract-matching approach,” IEEE Transactions on Intelligent Transportation Systems , vol. 22, no. 8, pp. 5140–5154, 2021
2021
Cited alongside, same era.
T. H. Thi Le, N. H. Tran, Y. K. Tun, M. N. H. Nguyen, S. R. Pandey, Z. Han, and C. S. Hong, “An incentive mechanism for federated learning in wireless cellular networks: An auction approach,” IEEE Transactions on Wireless Communications , vol. 20, no. 8, pp. 4874–4887, 2021
2021
Cited alongside, same era.
S. Feng, G. Tao, S. Cheng, G. Shen, X. Xu, Y. Liu, K. Zhang, S. Ma, and X. Zhang, “Detecting backdoors in pre-trained encoders,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023 . IEEE, 2023, pp. 16 352–16 362
2023
Later among the works it cites.
L. Liu, Y. Wang, G. Liu, K. Peng, and C. Wang, “Membership inference attacks against machine learning models via prediction sensitivity,” IEEE Trans. Dependable Secur. Comput. , vol. 20, no. 3, pp. 2341–2347, 2023
2023
Later among the works it cites.
H. Yan, S. Li, Y. Wang, Y. Zhang, K. Sharif, H. Hu, and Y. Li, “Membership inference attacks against deep learning models via logits distribution,” IEEE Trans. Dependable Secur. Comput. , vol. 20, no. 5, pp. 3799–3808, 2023
2023
Later among the works it cites.
J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu, “Are diffusion models vulnerable to membership inference attacks?” in International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 2023, pp. 8717–8730
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Yuan, K. Chen, J. Zhang, W. Zhang, N. Yu, and Y. Zhang, “Pseudo label-guided model inversion attack via conditional generative adversarial network,” in Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023 . AAAI Press, 2023, pp. 3349–3357
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.
S. D. Okegbile, J. Cai, H. Zheng, J. Chen, and C. Yi, “Differentially private federated multi-task learning framework for enhancing human-to-virtual connectivity in human digital twin,” IEEE J. Sel. Areas Commun. , vol. 41, no. 11, pp. 3533–3547, 2023
2023
Later among the works it cites.
X. Lin, J. Wu, J. Li, C. Sang, S. Hu, and M. J. Deen, “Heterogeneous differential-private federated learning: Trading privacy for utility truthfully,” IEEE Trans. Dependable Secur. Comput. , vol. 20, no. 6, pp. 5113–5129, 2023
2023
Later among the works it cites.
R. Hu, Y. Guo, and Y. Gong, “Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,” IEEE Transactions on Mobile Computing , 2023
2023
Later among the works it cites.
X. Zhang, Y. Kang, K. Chen, L. Fan, and Q. Yang, “Trading off privacy, utility, and efficiency in federated learning,” ACM Transactions on Intelligent Systems and Technology , vol. 14, no. 6, pp. 1–32, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Yu, J. Hong, Y. Zeng, F. Wang, R. Jia, and J. Zhou, “Who leaked the model? tracking ip infringers in accountable federated learning,” in NeurIPS 2023 Workshop on Regulatable ML , 2023
2023
Later among the works it cites.
X. Tang and H. Yu, “Utility-maximizing bidding strategy for data consumers in auction-based federated learning,” in Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME’23) , 2023
2023
Later among the works it cites.
X. Tang and H. Yu”, “Competitive-cooperative multi-agent reinforcement learning for auction-based federated learning,” in Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI’23) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Yi, G. Wang, X. Liu, Z. Shi, and H. Yu, “FedGH: Heterogeneous federated learning with generalized global header,” in Proceedings of the 31st ACM International Conference on Multimedia (ACM MM’23) , 2023, pp. 8686–8696
2023
Later among the works it cites.
J. Wang, S. Cui, and F. Ma, “Fedlego: Enabling heterogenous model cooperation via brick reassembly in federated learning,” in International Workshop on Federated Learning for Distributed Data Mining , 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.
M. Kuchnik, V. Smith, and G. Amvrosiadis, “Validating large language models with relm,” Proceedings of Machine Learning and Systems , vol. 5, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Younis, Z. Ahmadi, A. Hakmeh, and M. Fisichella, “Flames2graph: An interpretable federated multivariate time series classification framework,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Li, J. Huang, J. Jia, H. Peng, L. Zhang, L. A. Tuan, H. Yu, and X.-Y. Li, “Efficient and privacy-preserving feature importance-based vertical federated learning,” IEEE Transactions on Mobile Computing , no. 01, pp. 1–17, 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.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Zhao, “Privacy-preserving fine-tuning of artificial intelligence (ai) foundation models with federated learning, differential privacy, offsite tuning, and parameter-efficient fine-tuning (peft),” Authorea Preprints , 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.
2023
Later among the works it cites.
2023
Later among the works it cites.
C.-M. Feng, B. Li, X. Xu, Y. Liu, H. Fu, and W. Zuo, “Learning federated visual prompt in null space for mri reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8064–8073
2023
Later among the works it cites.
J. Shin, H. Yoon, S. Lee, S. Park, Y. Liu, J. D. Choi, and S.-J. Lee, “Fedtherapist: Mental health monitoring with user-generated linguistic expressions on smartphones via federated learning,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , 2023, pp. 11 971–11 988
2023
Later among the works it cites.
S. S. Azam, M. Pelikan, V. Feldman, K. Talwar, J. Silovsky, and T. Likhomanenko, “Federated learning for speech recognition: Revisiting current trends towards large-scale asr,” in International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 , 2023
2023
Later among the works it cites.
J. Jia, K. Li, M. Malek, K. Malik, J. Mahadeokar, O. Kalinli, and F. Seide, “Joint federated learning and personalization for on-device asr,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2023, pp. 1–8
2023
Later among the works it cites.
Y. Liu, X. Bi, L. Li, S. Chen, W. Yang, and X. Sun, “Communication efficient federated learning for multilingual neural machine translation with adapter,” in Findings of the Association for Computational Linguistics: ACL 2023 , 2023, pp. 5315–5328
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Gao, Y. Zhao, and H. Yu, “Multi-tier client selection for mobile federated learning networks,” in ICME , 2023
2023
Later among the works it cites.
C. Ren, H. Yu, R. Yan, Q. Li, Y. Xu, D. Niyato, and Z. Y. Dong, “Secfedsa: A secure differential privacy-based federated learning approach for smart cyber-physical grid stability assessment,” IEEE Internet of Things Journal , 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.
D. Wu, W. Yang, H. Jin, X. Zou, W. Xia, and B. Fang, “Fedcomp: A federated learning compression framework for resource-constrained edge computing devices,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2023
2023
Later among the works it cites.
Y. Mao, Z. Zhao, M. Yang, L. Liang, Y. Liu, W. Ding, T. Lan, and X.-P. Zhang, “Safari: Sparsity-enabled federated learning with limited and unreliable communications,” IEEE Transactions on Mobile Computing , 2023
2023
Later among the works it cites.
C. Xu, Y. Qu, Y. Xiang, and L. Gao, “Asynchronous federated learning on heterogeneous devices: A survey,” Computer Science Review , vol. 50, p. 100595, 2023
2023
Later among the works it cites.
Y. Liao, Y. Xu, H. Xu, Z. Yao, L. Wang, and C. Qiao, “Accelerating federated learning with data and model parallelism in edge computing,” IEEE/ACM Transactions on Networking , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Aziz, S. Banerjee, S. Bouzefrane, and T. Le Vinh, “Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm,” Future internet , vol. 15, no. 9, p. 310, 2023
2023
Later among the works it cites.
Y. Shi, H. Yu, and C. Leung, “Towards fairness-aware federated learning,” IEEE Transactions on Neural Networks and Learning Systems , 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.
N. Kuete Meli, F. Mannel, and J. Lellmann, “A universal quantum algorithm for weighted maximum cut and ising problems,” Quantum Information Processing , vol. 22, no. 7, p. 279, 2023
2023
Later among the works it cites.
R. Kaewpuang, M. Xu, D. Niyato, H. Yu, Z. Xiong, and X. S. Shen, “Adaptive resource allocation in quantum key distribution (qkd) for federated learning,” in 2023 International Conference on Computing, Networking and Communications (ICNC) . IEEE, 2023, pp. 71–76
2023
Later among the works it cites.
C. Ren, R. Yan, M. Xu, H. Yu, Y. Xu, D. Niyato, and Z. Y. Dong, “Qfdsa: A quantum-secured federated learning system for smart grid dynamic security assessment,” IEEE Internet of Things Journal , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
D. Myers, R. Mohawesh, V. I. Chellaboina, A. L. Sathvik, P. Venkatesh, Y.-H. Ho, H. Henshaw, M. Alhawawreh, D. Berdik, and Y. Jararweh, “Foundation and large language models: fundamentals, challenges, opportunities, and social impacts,” Cluster Computing , vol. 27, no. 1, pp. 1–26, 2024
2024
Closest in time.
2024
Closest in time.
S. Subramanian, P. Harrington, K. Keutzer, W. Bhimji, D. Morozov, M. W. Mahoney, and A. Gholami, “Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu, “Unifying large language models and knowledge graphs: A roadmap,” IEEE Transactions on Knowledge and Data Engineering , 2024
2024
Closest in time.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, 2024
2024
Closest in time.
M. AI, “Llama 3,” 2024. [Online]. Available: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3
2024
Closest in time.
OpenAI, “Gpt-4o,” 2024. [Online]. Available: https://platform.openai.com/docs/models/gpt-4o
2024
Closest in time.
M. AI, “Llama 3.1,” 2024. [Online]. Available: https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
H. Chen, Y. Zhang, D. Krompass, J. Gu, and V. Tresp, “Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 10, 2024, pp. 11 285–11 293
2024
Closest in time.
2024
Closest in time.
Y. He, Z. Shen, C. Xia, J. Hua, W. Tong, and S. Zhong, “SGBA: A stealthy scapegoat backdoor attack against deep neural networks,” Comput. Secur. , vol. 136, p. 103523, 2024
2024
Closest in time.
R. Xue, K. Xue, B. Zhu, X. Luo, T. Zhang, Q. Sun, and J. Lu, “Differentially private federated learning with an adaptive noise mechanism,” IEEE Trans. Inf. Forensics Secur. , vol. 19, pp. 74–87, 2024
2024
Closest in time.
W.-N. Chen, D. Song, A. Ozgur, and P. Kairouz, “Privacy amplification via compression: Achieving the optimal privacy-accuracy-communication trade-off in distributed mean estimation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
J. Sun, Z. Xu, H. Yin, D. Yang, D. Xu, Y. Liu, Z. Du, Y. Chen, and H. R. Roth, “Fedbpt: Efficient federated black-box prompt tuning for large language models,” in Forty-first International Conference on Machine Learning , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Du, Z. Zhang, L. Yue, X. Huang, Y. Zhang, T. Xu, L. Xu, and E. Chen, “Communication-efficient personalized federated learning for speech-to-text tasks,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 10 001–10 005
2024
Closest in time.
W. Zhao, Y. Chen, R. Lee, X. Qiu, Y. Gao, H. Fan, and N. D. Lane, “Breaking physical and linguistic borders: Multilingual federated prompt tuning for low-resource languages,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
Y.-W. Chu, D.-J. Han, and C. G. Brinton, “Only send what you need: Learning to communicate efficiently in federated multilingual machine translation,” in Companion Proceedings of the ACM on Web Conference 2024 , 2024, pp. 1548–1557
2024
Closest in time.
Y. Shen, J. Shao, X. Zhang, Z. Lin, H. Pan, D. Li, J. Zhang, and K. B. Letaief, “Large language models empowered autonomous edge ai for connected intelligence,” IEEE Communications Magazine , 2024
2024
Closest in time.
M. Chawla, G. R. Gupta, S. Gaddam, and M. Wadhwa, “Beyond federated learning for iot: Efficient split learning with caching & model customization,” IEEE INTERNET OF THINGS JOURNAL , p. 1, 2024
2024
Closest in time.
W. Zhang, T. Zhou, Q. Lu, Y. Yuan, A. Tolba, and W. Said, “Fedsl: A communication efficient federated learning with split layer aggregation,” IEEE Internet of Things Journal , 2024
2024
Closest in time.
Z. Charles, N. Mitchell, K. Pillutla, M. Reneer, and Z. Garrett, “Towards federated foundation models: Scalable dataset pipelines for group-structured learning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
S. A. Rieyan, M. R. K. News, A. M. Rahman, S. A. Khan, S. T. J. Zaarif, M. G. R. Alam, M. M. Hassan, M. Ianni, and G. Fortino, “An advanced data fabric architecture leveraging homomorphic encryption and federated learning,” Information Fusion , vol. 102, p. 102004, 2024
2024
Closest in time.
Z. Zhang, M. Liu, M. Sun, R. Deng, P. Cheng, D. Niyato, M.-Y. Chow, and J. Chen, “Vulnerability of machine learning approaches applied in iot-based smart grid: A review,” IEEE Internet of Things Journal , 2024
2024
Closest in time.
J. Tian, C. Shen, B. Wang, X. Xia, M. Zhang, C. Lin, and Q. Li, “Lesson: Multi-label adversarial false data injection attack for deep learning locational detection,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
Closest in time.
C. Ren, H. Xu, Minrui Yu, Z. Xiong, Z. Zhang, and D. Niyato, “Variational quantum circuit and quantum key distribution-based quantum federated learning: A case of smart grid dynamic security assessment,” International Conference on Communications , 2024
2024
Closest in time.
A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani, “Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2020, pp. 2021–2031
2031
Closest in time.