FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare (1907.09173v2)

Abstract: With the rapid development of computing technology, wearable devices such as smart phones and wristbands make it easy to get access to people's health information including activities, sleep, sports, etc. Smart healthcare achieves great success by training machine learning models on a large quantity of user data. However, there are two critical challenges. Firstly, user data often exists in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Secondly, the models trained on the cloud fail on personalization. In this paper, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds personalized models by transfer learning. It is able to achieve accurate and personalized healthcare without compromising privacy and security. Experiments demonstrate that FedHealth produces higher accuracy (5.3% improvement) for wearable activity recognition when compared to traditional methods. FedHealth is general and extensible and has the potential to be used in many healthcare applications.

• The paper introduces a novel approach combining federated learning and transfer learning to overcome data isolation and enhance personalization in wearable healthcare.
• It employs homomorphic encryption for secure model aggregation and fine-tunes higher layers to adapt to individual user data.
• Experimental results on the UCI Smartphone dataset show a 5.3% accuracy increase in activity recognition compared to traditional methods.

Federated Transfer Learning for Personal Healthcare Management

Overview

The paper introduces FedHealth, a federated transfer learning framework designed to address two pressing challenges in wearable healthcare: data isolation and the lack of personalization in healthcare models. The authors propose a novel approach that integrates federated learning with transfer learning to enhance model accuracy while maintaining user privacy.

Key Contributions

FedHealth is distinguished by its ability to aggregate decentralized data from various users without compromising on privacy, thanks to the federated learning approach. This framework enables the construction of robust machine learning models from distributed data silos. Moreover, FedHealth incorporates transfer learning to personalize models to individual users, thus ensuring higher accuracy in healthcare applications.

The experimental results highlight FedHealth's improved performance in wearable activity recognition, displaying a 5.3% accuracy increase compared to traditional methods. This improvement underscores the framework's efficacy in producing tailored and secure healthcare solutions.

Methodology

FedHealth's methodology involves several steps:

• Federated Learning: The initial cloud model is trained on public datasets and further refined by leveraging encrypted model parameters from users' data through homomorphic encryption. This process ensures user data remains private while contributing to a centralized model.
• Transfer Learning: To mitigate the divergence in data distribution between the cloud model and user-specific data, FedHealth employs transfer learning. This adaptation is facilitated by freezing lower-level layers and fine-tuning higher-level layers to capture personalized patterns in user data.

Numerical Results

The framework was evaluated using the UCI Smartphone dataset, which includes activity data from 30 users. The experimental setup involved five isolated users for whom personalized models were trained. FedHealth consistently outperformed other methods, achieving an average accuracy increase of 5.3% over traditional learning approaches, demonstrating the effectiveness of integrating federated learning and transfer learning techniques.

Implications and Future Directions

The implications of FedHealth extend to various sectors within personal healthcare management, including elderly care, cognitive disease detection, and activity monitoring. By ensuring privacy and personalization, FedHealth positions itself as a significant tool in the evolving landscape of wearable healthcare technologies.

Potential future developments include integrating incremental learning to continuously update models based on new user data and exploring blockchain technology to further safeguard user data. Additionally, FedHealth's framework can potentially become a standard in healthcare, enabling broader implementation across diverse applications.

Conclusion

FedHealth represents an innovative approach in wearable healthcare by effectively marrying federated learning with transfer learning. It addresses critical issues of privacy and personalization while demonstrating superior performance in activity recognition tasks. The paper's findings suggest promising avenues for future research and development, paving the way for more adaptive and secure healthcare management systems.