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This paper introduces \textit{Federated Retrieval-Augmented Generation (FRAG)}, a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems, which are increasingly powered by large-language models (LLMs).
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
How to Share a Secret
Adi Shamir. 1979 · 1979
Earlier work this paper cites.
Protocols for secure computations. In Proceedings of the 23rd Annual Symposium on Foundations of Computer Science (SFCS) . 160–164
Andrew Chi-Chih Yao. 1982 · 1982
Earlier work this paper cites.
Verifiable Secret Sharing and Multiparty Protocols with Honest Majority. In Proceedings of the Twenty-First Annual ACM Symposium on Theory of Computing (Seattle, Washington, USA) (STOC ’89) . Association for Computing Machinery, New York, NY, USA, 73–85
T. Rabin and M. Ben-Or. 1989 · 1989
Earlier work this paper cites.
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In Proceedings of the 17th International Conference on Theory and Application of Cryptographic Techniques (Prague, Czech Republic) (EUROCRYPT’99) . Springer-Verlag, Berlin, Heidelberg, 223–238
Pascal Paillier. 1999 · 1999
Earlier work this paper cites.
Fully homomorphic encryption using ideal lattices
Craig Gentry. 2009c · 2009
Earlier work this paper cites.
Redis: In-Memory Data Structure Store
Salvatore Sanfilippo. 2009 · 2009
Earlier work this paper cites.
Somewhat Practical Fully Homomorphic Encryption
Junfeng Fan and Frederik Vercauteren. 2012 · 2012
Earlier work this paper cites.
ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In 22nd Annual Network and Distributed System Security Symposium, NDSS 2015, San Diego, California, USA, February 8-11, 2015 . The Internet Society
Daniel Demmler, Thomas Schneider, and Michael Zohner. 2015 · 2015
Earlier work this paper cites.
Data poisoning attacks against autoregressive models. In Proceedings of the AAAI Conference on Artificial Intelligence
Scott Alfeld, Xiaojin Zhu, and Paul Barford. 2016 · 2016
Earlier work this paper cites.
SecureML: A Framework for Privacy-Preserving Machine Learning. In IEEE Security & Privacy
R. B. Smith et al. 2016 · 2016
Earlier work this paper cites.
Annoy: Approximate nearest neighbors in C++/Python
Michael Bernardini and colleagues. 2017 · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 1175–1191
Keith Bonawitz, Vladimir Ivanov, Benjamin Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017 · 2017
Earlier work this paper cites.
Homomorphic Encryption for Arithmetic of Approximate Numbers. In 23rd International Conference on the Theory and Applications of Cryptology and Information Security (AsiaCrypt) , Tsuyoshi Takagi and Thomas Peyrin (Eds.). Springer
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yong Soo Song. 2017 · 2017
Earlier work this paper cites.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017 · 2017
Earlier work this paper cites.
SecureML: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 19–38
Payman Mohassel and Yupeng Zhang. 2017a · 2017
Earlier work this paper cites.
SecureML: A System for Scalable Privacy-Preserving Machine Learning. In 2017 IEEE Symposium on Security and Privacy (SP) . 19–38
Payman Mohassel and Yupeng Zhang. 2017b · 2017
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Y. Malkov and D. A. Yashunin. 2018a · 2018
Cited alongside, same era.
Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
Yu A Malkov and D A Yashunin. 2018b · 2018
Cited alongside, same era.
Deepsecure: Scalable Provably-Secure Deep Learning. In Proceedings of the 55th Annual Design Automation Conference (San Francisco, California) (DAC ’18) . Association for Computing Machinery, New York, NY, USA, Article 2, 6 pages
Bita Darvish Rouhani, M. Sadegh Riazi, and Farinaz Koushanfar. 2018 · 2018
Cited alongside, same era.
BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20) . USENIX Association
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020 · 2020
Later among the works it cites.
TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption
Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, and Alaa Eddine Belfedhal. 2021 · 2021
Later among the works it cites.
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 2478–2488
Deepesh Data and Suhas Diggavi. 2021 · 2021
Later among the works it cites.
Milvus: A Purpose-Built Vector Data Management System. In SIGMOD . 2614–2627
J. Wang et al. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. In Proceedings of the 35th International Conference on Machine Learning (ICML) (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, 5650–5659
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018 · 2018
Cited alongside, same era.
Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. 2019 · 2019
Cited alongside, same era.
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI) (Honolulu, Hawaii, USA) (AAAI’19/IAAI’19/EAAI’19) . AAAI Press, Article 190, 8 pages
Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, and Qing Ling. 2019 · 2019
Cited alongside, same era.
Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
Cited alongside, same era.
How To Backdoor Federated Learning. In The 23rd International Conference on Artificial Intelligence and Statistics (AIStat) , Silvia Chiappa and Roberto Calandra (Eds.)
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
Cited alongside, same era.
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. In Proceedings of the 29th USENIX Conference on Security Symposium . USENIX Association, USA, Article 92, 18 pages
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. 2020 · 2020
Cited alongside, same era.
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr. 2020 · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems , Vol. 33. 9459–9474
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Alěna Kučerová, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Cited alongside, same era.
Federated learning: Challenges, methods, and future directions
Peter Kairouz, H Brendan McMahan, et al · 2021
Later among the works it cites.
Weaviate: Graph-based Vector Database
H. Kumar and Y. Jain. 2021 · 2021
Later among the works it cites.
Federated search: A privacy-preserving solution for distributed retrieval. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1735–1738
Qingqing Liu, Can Xu, et al · 2021
Later among the works it cites.
http://www.open-mpi.org/
Open MPI. Accessed 2021 · 2021
Later among the works it cites.
EuclidesDB: Managing Embedding Models for Vector Data Search
Jie Pan, Jianguo Wang, and Guoliang Li. 2021 · 2021
Later among the works it cites.
Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning. In Network and Distributed Systems Security (NDSS) Symposium 2021
Virat Shejwalkar and Amir Houmansadr. 2021 · 2021
Later among the works it cites.
Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective
Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li. 2021 · 2021
Later among the works it cites.
BASGD: Buffered Asynchronous SGD for Byzantine Learning. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 11751–11761
Yi-Rui Yang and Wu-Jun Li. 2021 · 2021
Later among the works it cites.
Qdrant: A Vector Database for High-Performance Vector Search
Q. Yu and X. Zhang. 2021 · 2021
Later among the works it cites.
Differentially private machine learning and homomorphic encryption: A survey of privacy-preserving techniques
Rongxing Zhang et al · 2021
Later among the works it cites.
GPT-4 Technical Report
OpenAI. 2023 · 2023
Later among the works it cites.
LLaMA: Open and efficient foundation language models
Hugo Touvron et al · 2023
Later among the works it cites.