Skip to main content
SpringerLink
Log in

A privacy-preserving multi-agent updating framework for self-adaptive tree model

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

The tree-based model is widely applied in classification and regression problems because of its interpretability. Self-adaptive forest models are proposed for adapting to dynamic environments by using active learning and online learning techniques. However, most existing self-adaptive forest models are designed under a single-agent situation. With the development of the IoT, data is distributed across multiple edge devices without geographic restrictions. A global model is trained by distributed data across multiple devices. Therefore, extending a single-agent self-adaptive forest model to a multi-agent one is useful to make the original tree-based models glow with new vitality. In a multi-agent system, the privacy-preserving problem should be addressed when sharing knowledge between agents. In this paper, we propose PMSF, a privacy-preserving multi-agent self-adaptive forest framework via federated learning. We utilize differential privacy to prevent attackers from getting the data statistics. No private data is uploaded into the server in our framework and only updated parameters are uploaded. Finally, We design local adaptation and global update procedures to ensure the ability of self-adaptation of the forest model and the ability of privacy protection in each agent, which can further improve the performance of self-adaptive forest models. To demonstrate the superiority and effectiveness of our framework, we conduct extensive experiments in an identity authentication case with two datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Tan SC, Ting KM, Liu TF (2011) Fast anomaly detection for streaming data. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence 22:1511–1516

  2. Das S, Wong WK, Fern A, Dietterich TG, Siddiqui MA (2017) Incorporating feedback into tree-based anomaly detection. arXiv preprint arXiv:1708.09441

  3. Pevnỳ T (2016) Loda: Lightweight on-line detector of anomalies. Mach Learn 102(2):275–304

    Article  MathSciNet  Google Scholar 

  4. Sun X, Li Y, Wang N, Li Z, Liu M, Gui G (2020) Towards self-adaptive selection of kernel functions for support vector regression in iot based marine data prediction. IEEE Internet Things J

  5. Wang H, Li Y, Qian J (2019) Self-adaptive resource allocation in underwater acoustic interference channel: A reinforcement learning approach. IEEE Internet Things J 7(4):2816–2827

    Article  Google Scholar 

  6. Shan J, Zhang H, Liu W, Liu Q (2018) Online active learning ensemble framework for drifted data streams. IEEE Transactions on Neural Networks and Learning Systems 30(2):486–498

    Article  Google Scholar 

  7. Yu H, Yang X, Zheng S, Sun C (2018) Active learning from imbalanced data: A solution of online weighted extreme learning machine. IEEE Transactions on Neural Networks and Learning Systems 30(4):1088–1103

    Article  Google Scholar 

  8. Bader-El-Den M (2014) Self-adaptive heterogeneous random forest. In: 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), pp. 640–646. IEEE

  9. Lahmar I, Zaier A, Yahia M, Bouallegue R (2019) A self adaptive fcm cluster forests based feature selection. In: 2019 IEEE 19th Mediterranean Microwave Symposium (MMS), pp. 1–4. IEEE

  10. Jie Z, Liang X, Feng J, Jin X, Lu W, Yan S (2016) Tree-structured reinforcement learning for sequential object localization. In: Advances in Neural Information Processing Systems, pp. 127–135

  11. Li Q, Yu Z, Yao L, Guo B (2021) Rltir: Activity-based interactive person identification via reinforcement learning tree. IEEE Internet Things J

  12. Liu G, Schulte O, Zhu W, Li Q (2018) Toward interpretable deep reinforcement learning with linear model u-trees. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 414–429. Springer

  13. Xiong Z, Zhang W, Zhu W (2017) Learning decision trees with reinforcement learning. In: NIPS Workshop on Meta-Learning

  14. McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR

  15. Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE

  16. Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R news 2(3):18–22

    Google Scholar 

  17. Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794

  18. Guha S, Mishra N, Roy G, Schrijvers O (2016) Robust random cut forest based anomaly detection on streams. In: International conference on machine learning, pp. 2712–2721

  19. Wu K, Zhang K, Fan W, Edwards A, Philip SY (2014) Rs-forest: A rapid density estimator for streaming anomaly detection. In: Data Mining (ICDM), 2014 IEEE International Conference on, pp. 600–609. IEEE

  20. Abdulsalam H, Skillicorn DB, Martin P (2010) Classification using streaming random forests. IEEE Trans Knowl Data Eng 23(1):22–36

    Article  Google Scholar 

  21. Li Q, Wu Z, Wen Z, He B (2020) Privacy-preserving gradient boosting decision trees. In: Proceedings of the AAAI Conference on Artificial Intelligence 34:784–791

  22. Truex S, Liu L, Gursoy ME, Yu L (2017) Privacy-preserving inductive learning with decision trees. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 57–64. IEEE

  23. Vaidya J, Clifton C (2005) Privacy-preserving decision trees over vertically partitioned data. In: IFIP Annual Conference on Data and Applications Security and Privacy, pp. 139–152. Springer

  24. Wu DJ, Feng T, Naehrig M, Lauter KE (2016) Privately evaluating decision trees and random forests. Proc Priv Enhancing Technol 2016(4):335–355

    Article  Google Scholar 

  25. Liu L, Chen R, Liu X, Su J, Qiao L (2020) Towards practical privacy-preserving decision tree training and evaluation in the cloud. IEEE Trans Inf Forensics Secur 15:2914–2929

    Article  Google Scholar 

  26. Kim H, Park J, Bennis M, Kim SL (2019) Blockchained on-device federated learning. IEEE Commun Lett 24(6):1279–1283

    Article  Google Scholar 

  27. Liu X, Li H, Xu G, Lu R, He M (2020) Adaptive privacy-preserving federated learning. Peer-to-Peer Networking and Applications 13(6):2356–2366

    Article  Google Scholar 

  28. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10(2):1–19

    Article  Google Scholar 

  29. Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang YC, Yang Q, Niyato D, Miao C (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun Surv Tutorials 22(3):2031–2063

    Article  Google Scholar 

  30. Liu Y, Ma Z, Liu X, Ma S, Nepal S, Deng RH, Ren K (2020) Boosting privately: Federated extreme gradient boosting for mobile crowdsensing. In: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), pp. 1–11. IEEE

  31. Wang S, Tuor T, Salonidis T, Leung KK, Makaya C, He T, Chan K (2019) Adaptive federated learning in resource constrained edge computing systems. IEEE J Sel Areas Commun 37(6):1205–1221

    Article  Google Scholar 

  32. Chen Y, Qin X, Wang J, Yu C, Gao W (2020) Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intell Syst 35(4):83–93

    Article  Google Scholar 

  33. Latif S, Khalifa S, Rana R, Jurdak R (2020) Federated learning for speech emotion recognition applications. In: 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 341–342. IEEE

  34. Zhu X, Wang J, Hong Z, Xia T, Xiao, J (2019) Federated learning of unsegmented chinese text recognition model. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1341–1345. IEEE

  35. Feng J, Rong C, Sun F, Guo D, Li Y (2020) Pmf: A privacy-preserving human mobility prediction framework via federated learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4(1):1–21

    Article  Google Scholar 

  36. Wu Y, Cai S, Xiao X, Chen G, Ooi BC (2020) Privacy preserving vertical federated learning for tree-based models. arXiv preprint arXiv:2008.06170

  37. Liu Y, Liu Y, Liu Z, Liang Y, Meng C, Zhang J, Zheng Y (2020) Federated forest. IEEE Transactions on Big Data

  38. Cheng K, Fan T, Jin Y, Liu Y, Chen T, Papadopoulos D, Yang Q (2021) Secureboost: A lossless federated learning framework. IEEE Intell Syst

  39. Liu Y, Ma Z, Yang Y, Liu X, Ma J, Ren K (2021) Revfrf: Enabling cross-domain random forest training with revocable federated learning. IEEE Transactions on Dependable and Secure Computing

  40. Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp. 308–318

  41. Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double q-learning. In: Thirtieth AAAI conference on artificial intelligence

  42. Bellman R (1966) Dynamic programming. Science 153(3731):34–37

    Article  Google Scholar 

  43. Zhu L, Han S (2020) Deep leakage from gradients. In: Federated Learning, pp. 17–31. Springer

  44. Dwork C, Roth A et al (2014) The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 9(3–4):211–407

    MathSciNet  MATH  Google Scholar 

  45. Xu W, Yu Z, Wang Z, Guo B, Han Q (2019) Acousticid: Gait-based human identification using acoustic signal. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3(3):1–25

    Google Scholar 

  46. Killourhy K, Maxion R (2010) Why did my detector do that?! In: International Workshop on Recent Advances in Intrusion Detection, pp. 256–276. Springer

  47. Anzar S, Amala K, Rajendran R, Mohan A, Ajeesh P, Aziz F et al (2016) Efficient online and offline template update mechanisms for speaker recognition. Comput Electr Eng 50:10–25

    Article  Google Scholar 

  48. Hossain HS, Al Haiz Khan MA, Roy N (2018) Deactive: scaling activity recognition with active deep learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2(2):1–23

  49. Gupta JK, Egorov M, Kochenderfer M (2017) Cooperative multi-agent control using deep reinforcement learning. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 66–83. Springer

Download references

Author information

Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Qingyang Li, Bin Guo & Zhu Wang

Authors
  1. Qingyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingyang Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Q., Guo, B. & Wang, Z. A privacy-preserving multi-agent updating framework for self-adaptive tree model. Peer-to-Peer Netw. Appl. 15, 921–933 (2022). https://doi.org/10.1007/s12083-021-01256-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-021-01256-6

Keywords

Search

Navigation