Pawanpreet Sangari | 10/14/2024Federated learning (FL) is a decentralized approach to machine learning that enables organizations to collaborate on training machine learning models while keeping their proprietary data safe and secure at the source. Federated learning fosters inclusive research by combining diverse datasets, enabling researchers to uncover new patterns, trends, and correlations. This approach can help patients, doctors, and researchers with faster diagnosis, enriched decision-making, and more informed, inclusive research work on health issues.
Figure 1 – The following diagram illustrates the example training process in Federated Learning
- Global Model Dissemination: A global federated learning model is distributed among participating organizations.
- Local Training: Each organization trains the local model on its individual dataset then summarizes and encrypts the model’s new configuration.
- Model Return: The locally trained models are returned to the central server for aggregation and integration into the centralized model.
- Final Global Model: The final global model is created by aggregating the local models, which can be used for making predictions and further training.
The collaborative training continues iteratively as illustrated in Figure 1; until the model is fully trained.
Features of Federated Learning
Decentralization: FL is designed to be decentralized, allowing multiple organizations to train a shared model without aggregating their individual datasets.
Data Protection: Each organization retains control over its own data, which remains protected behind the organization’s firewalls or Virtual Private Cloud (VPC).
Model Sharing: The trained machine learning model and its metadata are shared between organizations, enabling collaboration and knowledge sharing.
Client-Server Architecture: Each participating organization acts as a client, sending updates to a central server that aggregates the model weights.
Model Aggregation: The process of combining model weights from individual clients to create an updated global model.
Differences between Federated Learning and Machine Learning
| Features | Machine Learning | Federated Learning |
| Data Privacy | Data is sent to a central cloud server, posing privacy risks | Participants train local models cooperatively on their own data without sharing sensitive information with a central server. |
| Data Distribution | Assumes independent and identically distributed (i.i.d.) data among participants | Assumes non-i.i.d. data due to varying user data types, splitting the number of shards among participants for equal information distribution. |
| Continual Learning | Trains a central model using all available training data, suitable for long communication times with a central server. | It is difficult to implement continuous learning on end-user devices since they don’t have access to complete datasets. |
| Aggregation of Data Sets | Aggregates user data in a central location, potentially violating privacy rules and increasing data vulnerability to breaches. | Upgrades models constantly without aggregating data, allowing client input and no need for continuous learning. |
Types of Federated Learning
a. Centralized Aggregation Server federated learning: Centralized federated learning requires a central server. It coordinates the selection of client devices in the beginning and gathers the model updates during training. Communication happens only between the central server and individual edge devices.
While this approach looks straightforward and generates accurate models, the central server poses a bottleneck problem—network failures can halt the complete process.
b. Decentralized Peer to Peer federated learning: Decentralized federated learning does not require a central server to coordinate the learning. Instead, the model updates are shared only among the interconnected edge devices. The final model is obtained on an edge device by aggregating the local updates of the connected edge devices.
This approach prevents the possibility of a single-point failure; however, the model’s accuracy is completely dependent on the network topology of the edge devices.
Federated learning uses a variety of algorithms to optimize the training process. Some popular ones include:
Federated Stochastic Gradient Descent (FedSGD): A distributed version of stochastic gradient descent that updates the global model using client-server communication.
Federated Average Stochastic Gradient Descent (FedAvg): An extension of FedSGD that uses a weighted average of client gradients to improve model convergence. Instead of sending gradients, clients share their tuned model parameters.
These algorithms enable the efficient and secure training of machine learning models across multiple devices, promoting collaboration and reducing data silos in various applications.
Benefits of Federated Learning in Healthcare
Federated learning is a significant change in medical data settings, offering numerous benefits that enhance security, performance, and resilience.
Security and Privacy Features
- Data Protection: FL keeps sensitive data offline, ensuring it is not exposed to the internet while still leveraging its intelligence with differential privacy.
- Unbiased Models: FL builds, trains, and deploys robust models without compromising data security or facing hazards from cross-site data sharing.
- Multi-Vendor Management: With FL, we can overcome the challenges of multiple vendors managing data by preserving privacy and offering secure multi-party computation with local training.
Performance and Improvements
- Small Sample Size Problem: Address the issue of small sample sizes in medical imaging and costly labeling processes.
- Data Balancing: Balance the distribution of data to ensure fair representation of all patients.
- Traditional ML Methods: Incorporate most traditional machine learning and deep learning methods for improved results.
Resilience Benefits
- Party Flexibility: If one party decides to leave, it will not hinder training. New hospitals or institutes can join at any time without relying on specific datasets. No infrastructure lift required for legacy data scattered across geographical locations.
Use Cases of Federated Learning in Healthcare
Unlocking Interoperability in Healthcare
In recent years, government policies have emphasized the importance of data interoperability, highlighting the need for cross-organizational collaboration, and sharing. Federated learning (FL) is poised to play a crucial role in addressing this challenge.
FL can help medical institutions and agencies worldwide overcome data silos by providing seamless and secure integration and data interoperability. This enables the use of medical data for impactful machine learning-based predictions and pattern recognition, applicable to images and electronic health records (EHRs) alike.
Unlocking the Power of Legacy Data
Good artificial intelligence (AI) starts with good data. However, legacy systems in the federal domain often pose significant challenges when trying to derive good intelligence from these datasets. Legacy data can be a bottleneck in providing valuable insights to leaders, leading to inaccurate decision-making. Manually consolidating and integrating datasets across hospitals and institutes can take months or even years, resulting in missed opportunities for accurate decision-making and reliable AI. Legacy data holds important contextual information needed for accurate decision-making and well-informed model training. This long-term contextual information is crucial for identifying patterns and variations that might otherwise go undetected, leading to biased and inaccurate predictions.
Federated learning can break down these data silos and unite the untapped potential of scattered data, saving and transforming many lives. By sharing insights from isolated datasets, federated learning enables informed decisions on research direction and diagnosis, while creating a centralized repository of intelligence via a secure, private, and global knowledge base.
Challenges in Federated Learning
While offering significant advantages, federated learning presents a delicate balance between privacy and accuracy. Below, we will look at some of the challenges of federated learning and provide tips on how to deal with them.
Data heterogeneity
Distributed data across multiple devices can lead to non-IID (independent and identically distributed) and unbalanced datasets, making model training and performance challenging.
To overcome this, techniques like advanced data sampling and model personalization can be employed to ensure more uniform model training and performance across diverse data sets.
Communication overhead
The iterative process of updating and aggregating models across multiple devices requires significant communication bandwidth, which can be a bottleneck in environments with limited network resources.
Optimizing communication protocols, such as using model compression techniques or updating models less frequently, can help reduce this overhead.
Vulnerability to advanced cybersecurity threats
Federated learning is not immune to privacy risks, including sophisticated attacks like model inversion or differential attacks.
To prevent this from happening, consider employing advanced encryption methods and differential privacy techniques to bolster data security and mitigate the risks of privacy breaches.
Model and system complexity
Managing complex models across numerous devices is challenging, and large-scale federated learning systems require efficient algorithms and robust infrastructure.
However, utilizing more scalable and efficient machine learning algorithms, as well as advanced cloud-based infrastructures can aid in coping with these complexities.
Conclusion
Federated learning is a promising approach to distributed machine learning that enables collaboration between organizations while protecting data privacy. By leveraging key concepts like federated data, client-server architecture, and model aggregation, developers can create more accurate models for various industries.
Federated learning bridges the gap between isolated datasets, harnessing the unified powers of distributed datasets to improve efficiency and scalability without heavy infrastructure lifts. This approach enables machine learning to reach its full potential at the clinical level, not just research.
While challenges persist, advancements in algorithms and communication protocols are expected to improve the efficacy of federated learning. At Analytica, we expertly apply these concepts to drive innovative solutions. Contact us today to learn more about how our team can help you unlock the full potential of Federated Learning for your organization.
About Analytica:
As one of a select group of companies capable of bridging the gap between functional silos, Analytica specializes in providing a holistic approach to an organization’s financial, analytics, and information technology needs. We are an SBA-certified 8(a) small business that supports public-sector civilian, national security, and health missions. We are committed to ensuring quality and consistency in the services and technologies we deliver. We demonstrate this commitment through our appraisal at the Software Engineering Institute’s CMMI® V2.0 Maturity Level 3, ISO 9001:2015, ISO/IEC 20000-1:2018, ISO/IEC 27001:2013, and ITIL certification.
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