In this blog, we'll explore how Federated Learning can solve the common challenges around data access. It’s no secret that lack of access to the right data is one of the leading causes of project failures; what is surprising is that the right data often exists in data silos that, frustratingly, data scientists cannot access.
Federated learning provides a solution to this challenge by enabling machine learning (ML) across data silos. That’s because, while traditional ML requires all data to be centralized before training a model, federated learning trains models on distributed datasets, where data isn’t required to move from its original location.
Why federated learning is worth your time
This approach offers several advantages, including the ability to comply with privacy regulations, protect data security and sensitive IP, and avoid the pain and expense of moving data. It enables businesses to work seamlessly across boundaries as they build, deploy, and fine-tune their ML-based products, regardless of physical or virtual boundaries across organizations, industries, and even geographies.
What's more, it radically reduces the costs and risks typically associated with centralizing data, creating a safe and resource-light environment to create new ML-enabled products and run models on a federated infrastructure.
When and where to utilize federated learning
When it comes to ML, there are countless areas that could benefit from federated learning. In fact, this powerful technology has the potential to transform any use case where ML is already employed or being considered.
For instance, federated learning is an excellent fit for organizations looking to re-train or enhance ML models using sensitive data to accelerate the drug discovery pipeline or assess credit risk. Similarly, it’s the perfect fit for jointly learning from distributed, sensitive datasets among consortium partners while protecting each party’s IP and data privacy. For example:
When it comes to drug discovery, organizations can use federated learning to re-train protein structure prediction models (or other pretrained large language models) on sensitive third-party data, with no risk to IP. This approach also allows for enhancing ML models across a variety of data modalities, including multi-omics, molecular data and clinical trial data, to name a few.
For medical imaging, participants can accelerate the development of robust and generalizable diagnostics or clinical decision support applications, and can easily train weakly supervised AI models to assist with segmentation, classification or diagnosis in digital pathology.
The Fintech industry can harness federated learning to better collaborate with financial institutions in order to fine-tune their ML models on previously out-of-reach sensitive customer data. Training robust and interpretable ML models on this third-party consumer data also allows Fintech organizations to better assess credit risk, and work collaboratively across borders without moving data.
Those are just some examples of federated learning in action, but what if your team can't build a powerful enough ML model for your use case? Perhaps you've reached a point where your team can't build any model at all, or your model isn't accurate enough to meet your business objectives. In these instances, federated learning can help you achieve your desired outcomes through safe access to complementary datasets.
Another common roadblock to building better models is the lack of sufficient data. If you find yourself in this situation, it's possible that your team has already extracted as much value as possible from the data at hand, and your model's performance cannot improve without additional training data. Alternatively, your data science team may know what data they need to improve the model, but they need to take a federated approach to unlock access to it.
Other data silos can present a similar significant challenge when it comes to ML. Even if you know the data you need exists, your team may not be able to access it due to data fragmentation, regulatory restrictions, or other trust and technical limitations.
If you find yourself up against any of these challenges, chances are that federated learning is the solution you need to unlock new ML models, better analytics, and new product opportunities.
How to introduce federated learning into your data ecosystem
Now that you see the potential of federated learning, and have a clear idea of how it can be used to innovate, accelerate, and overcome typical data sharing challenges, it’s time to choose the right platform to inject federated learning into your data ecosystem. Apheris offers such a solution, through a federated ML and analytics platform that can help your organization achieve its data collaboration goals while providing a secure and efficient way to run analysis and ML models – regardless of boundaries.
Our platform is designed to seamlessly integrate with your existing tools and workflows, allowing you to leverage existing models, automate model training, and build data applications across organizations, geographies, and use cases with ease. We recognize that each organization has different needs, which is why we offer different deployment modes, including on-premises and various cloud environments.
Our mission at Apheris is to help you access the data you need to unlock the AI and ML insights you can't yet reach. With our federated learning platform, you can achieve positive, rapid ROI by unlocking new insights, launching new data collaborations, and driving new revenue from your ML and AI projects. And with all our deployments available as Infrastructure-As-Code, we can support and configure deployments on behalf of customers, ensuring a smooth and hassle-free experience.
Federated vs centralized