Federated and Swarm Learning for Privacy AI Computing in Healthcare
Authors: Amina Khalpey, PhD, Zain Khalpey MD, PhD, Brynne Rozell BS, Jim Whitfill, MD
Artificial Intelligence (AI) has the potential to revolutionize the healthcare industry by providing more efficient, accurate and personalized medical care. Not only could Ai be capable of prognosticating and predicting surgical complications it could provide surgical assistance during operations, assist physician decision making and augment ICU care. However, the increasing use of AI in healthcare raises concerns about privacy, security and bias. To address these issues, researchers have developed innovative privacy-preserving methods, including Federated Learning and Swarm Learning.
Federated Learning is a privacy-preserving machine learning method that allows data to be shared and used without being centralized. Instead of storing data in a single location, Federated Learning splits the data into multiple decentralized locations, allowing AI algorithms to be trained on this data without accessing the raw data itself, thereby preserving patient privacy. This also allows an unprecedented amount of data to be combined from multiple locations.
Advantages of utilizing Federated Learning in Healthcare include:
Increased Privacy: Federated Learning allows for the sharing of information between organizations without exposing the raw data to others. This protects the privacy of patients and the security of sensitive medical information.
Reduced Bias: Unconscious bias can slip into the training data and therefore the AI learning models. Models will need monitoring by separate teams from those who develop them so they can look for bias. This means that models developed in one population need to be validated when brought into new sites/populations. Federated Learning can reduce the potential for bias in AI algorithms by combining data from multiple sources, increasing the diversity of the data and reducing the risk of algorithm overfitting to a single dataset.
Improved Accuracy:Federated Learning allows for the training of larger and more diverse datasets, increasing the accuracy of AI algorithms and providing more comprehensive insights into medical data.
Disadvantages to implementing Federated Learning into healthcare which should be considered:
Complex Implementation: Federated Learning requires a complex infrastructure and technical expertise to implement, making it a challenge for smaller organizations and medical facilities to adopt.
Latency: Federated Learning requires the sharing of information between decentralized locations, which can result in latency and slow performance.
Trust: Federated Learning requires trust between participating organizations, as they must share information without exposing raw data to others.
The Black Box of Ai: This is a term used to describe the lack of transparency in AI algorithms and their decision-making processes. This lack of transparency makes it difficult to understand why the algorithm made a certain decision or how it arrived at a certain outcome. This can lead to problems of accountability and trust in AI-based systems. AI outputs that cannot explain their conclusion and how it was reached is problematic especially for doctors who are using this information to make complex clinical decisions.
Another privacy protecting method called Swarm Learning, is a privacy-preserving machine learning method that allows data to be shared and used without exposing the raw data to others. Swarm Learning uses a decentralized network of agents to perform machine learning tasks, allowing data to be processed locally and securely.
The advantages of Swarm Learning in healthcare include:
Increased Privacy: Swarm Learning allows for the processing of medical data without exposing the raw data to others, protecting the privacy of patients and the security of sensitive medical information.
Improved Scalability: Swarm Learning allows for the processing of large datasets, making it more scalable and flexible than traditional machine learning methods.
Reduced Latency: Swarm Learning allows for the processing of data locally, reducing latency and improving performance.
However there are also disadvantages with this strategy as well, such as:
Complex Implementation: Swarm Learning requires a complex infrastructure and technical expertise to implement, making it a challenge for smaller organizations and medical facilities to adopt.
Trust: Swarm Learning requires trust between participating organizations, as they must share information without exposing raw data to others.
Accuracy: Swarm Learning may result in reduced accuracy compared to traditional machine learning methods, as the data is processed locally and may not be as diverse as a centralized dataset.
While there may not be the perfect solution, both Federated Learning and Swarm Learning are innovative privacy-preserving methods for AI computing in healthcare. Both methods have advantages and disadvantages, and the choice of method will depend on the specific needs and constraints of the healthcare organization. However, as privacy and security become increasingly important in healthcare, Federated Learning and Swarm Learning are likely to play an important role in the development of AI algorithms that can provide more efficient, accurate and personalized medical care while protecting the privacy of patients and the security of sensitive medical information. The incorporation of AI into major industries like healthcare should be approached with deliberation and education, as it is not a replacement for human decision making. Users of AI need to be educated on the limits of AI and learn how to use it to augment human decision making.