Federated Unlearning for Human Activity Recognition
Abstract: The rapid evolution of Internet of Things (IoT) technology has spurred the widespread adoption of Human Activity Recognition (HAR) in various daily life domains. Federated Learning (FL) is frequently utilized to build a global HAR model by aggregating user contributions without transmitting raw individual data. Despite substantial progress in user privacy protection with FL, challenges persist. Regulations like the General Data Protection Regulation (GDPR) empower users to request data removal, raising a new query in FL: How can a HAR client request data removal without compromising other clients' privacy? In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data. Our method employs a third-party dataset unrelated to model training. Using KL divergence as a loss function for fine-tuning, we aim to align the predicted probability distribution on forgotten data with the third-party dataset. Additionally, we introduce a membership inference evaluation method to assess unlearning effectiveness. Experimental results across diverse datasets show our method achieves unlearning accuracy comparable to \textit{retraining} methods, resulting in speedups ranging from hundreds to thousands.
- A machine learning classifier for detection of physical activity types and postures during free-living. Journal for the Measurement of Physical Behaviour, 5(1):24–31, 2021.
- Iot wearable sensor and deep learning: An integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet of Things Journal, 6(5):8553–8562, 2019.
- Towards making systems forget with machine unlearning. In 2015 IEEE symposium on security and privacy, pages 463–480. IEEE, 2015.
- The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19), pages 267–284, 2019.
- Privacy preserving machine unlearning for smart cities. Annals of Telecommunications, 79(1):61–72, 2024.
- Cynthia Dwork. Differential privacy. In International colloquium on automata, languages, and programming, pages 1–12. Springer, 2006.
- Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pages 1322–1333, 2015.
- Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9304–9312, 2020.
- Federated unlearning: How to efficiently erase a client in fl? arXiv preprint arXiv:2207.05521, 2022.
- Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.
- Solomon Kullback. Information theory and statistics. Courier Corporation, 1997.
- Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
- Meta-har: Federated representation learning for human activity recognition. In Proceedings of the web conference 2021, pages 912–922, 2021.
- Hierarchical clustering-based personalized federated learning for robust and fair human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(1):1–38, 2023.
- Harth: a human activity recognition dataset for machine learning. Sensors, 21(23):7853, 2021.
- Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
- Clusterfl: a similarity-aware federated learning system for human activity recognition. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, pages 54–66, 2021.
- Stuart L Pardau. The california consumer privacy act: Towards a european-style privacy regime in the united states. J. Tech. L. & Pol’y, 23:68, 2018.
- Enhancing lifestyle and health monitoring of elderly populations using csa-tkelm classifier. Knowledge-Based Systems, 276:110758, 2023.
- Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:1806.01246, 2018.
- Federated multi-task attention for cross-individual human activity recognition. In IJCAI, pages 3423–3429. IJCAI, 2022.
- Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3–18. IEEE, 2017.
- Asynchronous federated unlearning. In IEEE INFOCOM 2023-IEEE Conference on Computer Communications, pages 1–10. IEEE, 2023.
- Feddl: Federated learning via dynamic layer sharing for human activity recognition. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, pages 15–28, 2021.
- Validation of an activity type recognition model classifying daily physical behavior in older adults: The har70+ model. Sensors, 23(5):2368, 2023.
- Paul Voigt and Axel Von dem Bussche. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 10(3152676):10–5555, 2017.
- Bfu: Bayesian federated unlearning with parameter self-sharing. In Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, pages 567–578, 2023.
- Federated unlearning with knowledge distillation. arXiv preprint arXiv:2201.09441, 2022.
- Machine unlearning: A survey. ACM Computing Surveys, 56(1):1–36, 2023.
- Arfdnet: An efficient activity recognition & fall detection system using latent feature pooling. Knowledge-Based Systems, 239:107948, 2022.
- A survey of federated unlearning: A taxonomy, challenges and future directions. arXiv preprint arXiv:2310.19218, 2023.
- Fedhar: Semi-supervised online learning for personalized federated human activity recognition. IEEE Transactions on Mobile Computing, 2021.
- Federated unlearning for on-device recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 393–401, 2023.
- The secret revealer: Generative model-inversion attacks against deep neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 253–261, 2020.
- Fedrecovery: Differentially private machine unlearning for federated learning frameworks. IEEE Transactions on Information Forensics and Security, 2023.
- Learning deep features for scene recognition using places database. Advances in neural information processing systems, 27, 2014.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
