“Enhancing Privacy in Federated Learning for Human Activity Recognition through Lightweight Machine Unlearning”

“Enhancing Privacy in Federated Learning for Human Activity Recognition through Lightweight Machine Unlearning”

The rapid evolution of Internet of Things (IoT) technology has led to the widespread adoption of Human Activity Recognition (HAR) in various daily life domains. Federated Learning (FL) has emerged as a popular approach for building global HAR models by aggregating user contributions without transmitting raw individual data. While FL offers improved user privacy protection compared to traditional methods, challenges still exist.

One particular challenge arises from regulations like the General Data Protection Regulation (GDPR), which grants users the right to request data removal. This poses a new question for FL: How can a HAR client request data removal without compromising the privacy of other clients?

In response to this query, 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 leverages a third-party dataset that is unrelated to model training. By employing 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 the effectiveness of the unlearning process. This evaluation method allows us to measure the accuracy of unlearning and compare it to traditional retraining methods.

To validate the efficacy of our approach, we conducted experiments using diverse datasets. The results demonstrate that our method achieves unlearning accuracy that is comparable to retraining methods. Moreover, our method offers significant speedups, ranging from hundreds to thousands.

Expert Analysis

This research addresses a critical challenge in federated learning, which is the ability for clients to request data removal while still maintaining the privacy of other clients. With the increasing focus on data privacy and regulations like GDPR, it is crucial to develop techniques that allow individuals to have control over their personal data.

The proposed lightweight machine unlearning method offers a practical solution to this challenge. By selectively removing a portion of a client’s training data, the model can be refined without compromising the privacy of other clients. This approach leverages a third-party dataset, which not only enhances privacy but also provides a benchmark for aligning the predicted probability distribution on forgotten data.

The use of KL divergence as a loss function for fine-tuning is a sound choice. KL divergence measures the difference between two probability distributions, allowing for effective alignment between the forgotten data and the third-party dataset. This ensures that the unlearning process is efficient and accurate.

The introduction of a membership inference evaluation method further strengthens the research. Evaluating the effectiveness of the unlearning process is crucial for ensuring that the model achieves the desired level of privacy while maintaining performance. This evaluation method provides a valuable metric for assessing the accuracy of unlearning and comparing it to retraining methods.

The experimental results presented in the research showcase the success of the proposed method. Achieving unlearning accuracy comparable to retraining methods is a significant accomplishment, as retraining typically requires significant computational resources and time. The speedups offered by the lightweight machine unlearning method have the potential to greatly enhance the efficiency of FL models.

Future Implications

The research presented in this article lays the groundwork for further advancements in federated learning and user privacy protection. The lightweight machine unlearning method opens up possibilities for other domains beyond HAR where clients may need to request data removal while preserving the privacy of others.

Additionally, the use of a third-party dataset for aligning probability distributions could be extended to other privacy-preserving techniques in federated learning. This approach provides a novel way to refine models without compromising sensitive user data.

Future research could explore the application of the proposed method in more complex scenarios and evaluate its performance in real-world settings. This would provide valuable insights into the scalability and robustness of the lightweight machine unlearning method.

In conclusion, the lightweight machine unlearning method proposed in this research offers a promising solution to the challenge of data removal in federated learning. By selectively removing a client’s training data and leveraging a third-party dataset, privacy can be preserved without compromising the overall performance of the model. This research paves the way for further advancements in privacy-preserving techniques and opens up possibilities for the application of federated learning in various domains.

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“Utilizing Federated Learning for Enhanced Oral Health Monitoring”

“Utilizing Federated Learning for Enhanced Oral Health Monitoring”

The article discusses the importance of oral hygiene in overall health and introduces a novel solution called Federated Learning (FL) for object detection in oral health analysis. FL is a privacy-preserving approach that allows data to remain on the local device while training the model on the edge, ensuring that sensitive patient images are not exposed to third parties.

The use of FL in oral health analysis is particularly crucial due to the sensitivity of the data involved. By keeping the data local and only sharing the updated weights, FL provides a secure and efficient method for training the model. This approach not only protects patient privacy but also ensures that the algorithm continues to learn and improve by aggregating the updated weights from multiple devices via The Federated Averaging algorithm.

To facilitate the application of FL in oral health analysis, the authors have developed a mobile app called OralH. This app allows users to conduct self-assessments through mouth scans, providing quick insights into their oral health. The app can detect potential oral health concerns or diseases and even provide details about dental clinics in the user’s locality for further assistance.

One of the notable features of the OralH app is its design as a Progressive Web Application (PWA). This means that users can access the app seamlessly across different devices, including smartphones, tablets, and desktops. The app’s versatility ensures that users can conveniently monitor their oral health regardless of the device they are using.

The application utilizes state-of-the-art segmentation and detection techniques, leveraging the YOLOv8 object detection model. YOLOv8 is known for its high performance and accuracy in detecting objects in images, making it an ideal choice for identifying oral hygiene issues and diseases.

This study demonstrates the potential of FL in the healthcare domain, specifically in oral health analysis. By preserving data privacy and leveraging advanced object detection techniques, FL can provide valuable insights into a patient’s oral health while maintaining the highest level of privacy and security. The OralH app offers a user-friendly platform for individuals to monitor their oral health and take proactive measures to prevent and address potential issues.

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