Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning

Core Concepts

A communication-efficient hybrid federated learning approach that effectively integrates horizontal and vertical federated learning to address the challenges of horizontally and vertically partitioned medical data in e-health.

Abstract

The content presents a hybrid federated learning framework for e-health that consists of a horizontal-vertical-horizontal structure. The proposed framework includes one intermediate result exchange and two aggregation phases to efficiently deal with horizontally and vertically partitioned medical data while reducing communication overhead. Specifically, the framework consists of the following key components: Intermediate result exchange phase: Hospitals and wearable devices communicate intermediate results to calculate partial derivatives without disclosing private patient information. Local aggregation phase: Edge nodes collect models trained on wearable devices within the same group to aggregate a uniform local device side model, improving training efficiency. Global aggregation phase: The cloud server aggregates local models to obtain a generalized global model, addressing the issue of heterogeneous data and insufficient samples in individual hospital-patient groups. Based on this framework, the authors develop a Hybrid Stochastic Gradient Descent (HSGD) algorithm to train models. The HSGD algorithm is theoretically analyzed, and its convergence upper bound is derived. Using the convergence results, the authors design three adaptive strategies to adjust the training parameters and shrink the size of transmitted data, further reducing communication cost while achieving the desired accuracy. The experimental results validate the effectiveness of the proposed HSGD algorithm and adaptive strategies, demonstrating their ability to achieve the desired accuracy while reducing communication cost and training time compared to several baselines.

Stats

"The global e-health market reached a value of USD 62.4 billion in 2021 and is expected to grow about 12 times by 2028." "About 87 million American residents experienced e-health services monthly in 2020, and the number is projected to steadily increase in the future."

Quotes

"E-health connects smart devices and healthcare providers via Internet-of-Things (IoT) technologies to offer intelligent health services." "E-health has a three-tier horizontal-vertical-horizontal data distribution structure." "Transmitting the raw data stored in different locations causes two issues: increased overhead and potential privacy leakage."

Key Insights Distilled From

Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning

by Chong Yu,Shu... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10110.pdf

Deeper Inquiries

How can the proposed hybrid federated learning framework be extended to handle non-IID data distributions in e-health

To extend the proposed hybrid federated learning framework to handle non-IID data distributions in e-health, several adjustments and considerations can be made. Non-IID data distributions in e-health can arise due to various factors such as demographic differences, medical conditions, and geographical locations of patients. One approach to address this is by incorporating techniques like data augmentation and transfer learning. By augmenting the data with synthetic samples or leveraging pre-trained models on related tasks, the model can adapt to the non-IID nature of the data. Additionally, federated learning algorithms can be modified to account for the diversity in data distribution across different hospital-patient groups. This can involve introducing weighting mechanisms to prioritize certain groups or adjusting the aggregation process to handle the varying data characteristics effectively. By enhancing the model's ability to generalize across diverse data distributions, the framework can better accommodate non-IID data in e-health settings.

What are the potential security and privacy risks associated with the intermediate result exchange in the hybrid federated learning approach, and how can they be mitigated

The intermediate result exchange in the hybrid federated learning approach poses potential security and privacy risks that need to be addressed to safeguard sensitive patient information. One major concern is the possibility of information leakage through the exchanged intermediate results, which could potentially reveal details about individual patients or compromise the confidentiality of medical data. To mitigate these risks, several strategies can be implemented. Encryption techniques such as homomorphic encryption can be utilized to secure the intermediate results during transmission, ensuring that the data remains confidential and protected from unauthorized access. Additionally, differential privacy mechanisms can be applied to add noise to the intermediate results, preserving privacy while still allowing for effective model training. By implementing robust security measures and privacy-preserving protocols, the risks associated with the intermediate result exchange can be minimized, ensuring the integrity and confidentiality of patient data in the federated learning process.

What other applications beyond e-health can benefit from the communication-efficient hybrid federated learning approach, and what are the unique challenges in those domains

The communication-efficient hybrid federated learning approach has applications beyond e-health that can benefit from its capabilities in handling distributed data while minimizing communication costs. One such application is in the financial sector, where multiple banks or financial institutions can collaborate to train models for fraud detection or risk assessment without sharing sensitive customer data. The unique challenge in this domain lies in ensuring compliance with regulatory requirements such as data privacy laws and financial regulations while still achieving accurate and efficient model training. Another potential application is in smart cities, where data from various IoT devices and sensors can be leveraged for urban planning, traffic management, and environmental monitoring. The challenge here is integrating data from diverse sources while maintaining data security and privacy. By adapting the hybrid federated learning approach to these domains and addressing the specific challenges they present, significant advancements can be made in leveraging distributed data for improved decision-making and insights.