Federated Learning (FL), also known as Collaborative Learning, is a privacy-preserving machine learning technique that aims at training a machine learning algorithm, for instance, deep neural networks, on multiple local datasets contained in local nodes without explicitly exchanging data samples. The general principle consists of training local models on local data samples and exchanging parameters (e.g., the weights of a neural network) between these local nodes at some frequency to generate a global model shared by all nodes.
There are several flavors of FL, including Centralized FL (where there is a single server coordinating and orchestrating the training process), Decentralized FL (where there is no centralized server and nodes are able to coordinate themselves to obtain a global model), and Heterogeneous FL (addressing the challenges of training across many heterogeneous clients with different hardware and networking capabilities).
We will provide an example of Centralized FL:
- The process begins with a proposed model to be trained on local datasets at different sites.
- The model is sent to each of the sites, where a number of iterations (epochs) of training are performed with the local data to generate a local model at each site.
- The model parameters (e.g., weights of a neural network) are sent to the federated server, which merges these parameters (e.g., using Federated Averaging, Federated SGD, or other aggregation algorithms) to create a new version of the proposed model.
- The new version of the proposed model is sent to the sites, repeating steps 2 and 3 a predefined number of times, or until the model has reached a certain performance threshold or the improvement achieved in each iteration has diminished below a certain threshold.
- The latest version of the proposed model is defined as the global model, which is the output of the FL process.
Federated Learning was proposed by Google in 2016 and has been increasingly used across many industries, from mobile devices (e.g., for predicting the next word typed on a mobile device keyboard), to IoT, autonomous vehicles, and even oil rigs.
Digital health is a natural fit for Federated Learning. Healthcare AI is being held back by a lack of access to clinically relevant data from a diverse patient population collected in different places and updated over time. The privacy-preserving characteristics of FL, where the underlying data is never moved, help overcome these challenges. The federated learning approach makes it possible for developers and researchers to collaborate across institutions without ever moving data, transferring ownership, or risking patient privacy. This approach to utilizing larger, more diverse datasets enables AI-based healthcare solutions to scale globally at an unprecedented pace. This powers AI models that perform more consistently and improve the standard of care. With federated learning, data remains at rest behind institutional firewalls to provide an additional physical layer of security to preserve patient privacy.
Advantages of Federated Learning
Federated Learning (FL) stands as a pioneering approach to machine learning with distinct advantages that have propelled it into diverse industries, including the healthcare domain. This section highlights the key benefits of adopting Federated Learning within the context of the Rhino Health Federated Computing Platform.
1. Privacy Preservation
The paramount advantage of Federated Learning lies in its unwavering commitment to data privacy. By keeping data localized and never moving it from its source, FL ensures that sensitive information remains within the secure boundaries of each institution. This privacy-preserving characteristic eliminates the need to centralize data, minimizing the risk of breaches and ensuring compliance with stringent data protection regulations.
2. Secure Collaboration
Federated Learning introduces a secure paradigm for collaboration across institutions. With FL, data scientists can collaboratively train machine learning models without sharing the raw data. The exchange of model parameters instead of data samples enables secure collaboration while upholding the confidentiality of sensitive information. The Rhino Health Federated Computing Platform takes this advantage further by facilitating secure communication throughout the Rhino Health network architecture, ensuring data integrity throughout the collaboration process.
3. Efficient Knowledge Sharing
Federated Learning empowers institutions to pool their collective knowledge without centralizing their data. This approach facilitates the creation of a global model that captures insights from diverse datasets, enhancing the overall performance and accuracy of the machine learning model. Rhino Health FCP's orchestration capabilities further streamline the knowledge-sharing process, allowing a seamless exchange of model parameters and iterative improvements.
4. Scalability and Diversity
The distributed nature of Federated Learning enables scalability on a global scale. Institutions with diverse datasets can contribute their unique insights without compromising their data security. This scalability fosters the development of robust and versatile machine-learning models that can cater to a wide range of scenarios and challenges within the healthcare landscape.
5. Faster Iterative Learning
Federated Learning's iterative training process accelerates the learning curve of machine learning models. With each iteration, the global model improves by incorporating collective knowledge from different sites. Rhino Health FCP's integration with Tensorboard allows data scientists to visualize these improvements, enabling them to fine-tune the models more effectively and efficiently.
6. Facilitating Regulatory Compliance
In the realm of healthcare, adhering to data privacy regulations is of utmost importance. Federated Learning introduces an approach that empowers model developers and data custodians to align with regulatory requirements seamlessly. By preserving data within institutional boundaries, Federated Learning makes it inherently easier for stakeholders to comply with data privacy regulations. This approach not only enhances the development of advanced machine learning models but also ensures that data remains under appropriate governance, fostering trust and accountability within the healthcare ecosystem.
7. Enhanced Security Layers
Rhino Health FCP, as a distributed MLOps platform, fortifies the security of Federated Learning by adding an additional layer of security through institutional firewalls. The platform ensures that data remains at rest within the secured boundaries of each institution, safeguarding patient privacy and confidential information.
Summary
Federated Learning redefines collaboration and knowledge sharing by preserving data privacy, enabling secure collaboration, and fostering efficient model improvements. Rhino Health FCP's integration of these advantages empowers data scientists to embark on groundbreaking research and innovation within the healthcare sector while upholding the highest standards of data security and compliance.