It’s no secret that we’re big believers in the power of data collaboration. While we’ve spent the last four years building a platform for federated and privacy-preserving data science to help organizations harness that power in a safe and secure way, many of them still come up against similar challenges when it comes to implementation.
In this blog, we’ll look at some common challenges organizations face when implementing federated learning and key considerations that can help them overcome those challenges and reap the rewards of working with federated data.
Privacy and security concerns
Just as two heads are better than one, multiple data sets that were previously locked away behind sensitivity concerns and regulatory requirements will undoubtedly unlock better insights than one ever could. The problem is that organizations looking to improve their own data sets with their partners or internally across different geographies are hesitant to divulge sensitive information, intellectual property (IP), and other company secrets. Besides the hard-to-achieve implicit trust, there are plenty of regional regulatory hoops to jump through to make this happen.
Federated learning alone isn’t the answer to such privacy and security concerns, but it goes some way in offering peace of mind by removing the need to move data. That being the case, there is still a need to implement additional tools to ensure that IP and other sensitive data cannot be leaked when the results are returned to the user.
To achieve this, organizations should strengthen their federated learning approach with a modular approach to privacy-enhancing technologies (PETs) via a federated infrastructure. In doing so, and by coupling identity and access management at the computational level to a federated and secure infrastructure, these organizations will be able to ensure maximum data insights with minimal privacy risks.
Security is, by its very nature, a vast, individual, and multi-dimensional beast, and no two organizations will have the exact same concerns. Because of this, there are a plethora of additional considerations to be taken into account, and it’s up to each organization to complement federated learning with the right IT security measures and tools to enable true data collaboration.
Federated learning requires plenty of compute and bandwidth availability, and organizations should be mindful of choosing a federated machine learning platform that uses efficient communication to overcome this challenge, regardless of their compute capabilities. The right platform will scale to high workloads, multiple computations, and various collaborations, without the need to move data out of the custodian’s environment. The wrong platforms won’t do this, as they aren’t easily integrated into the necessary workflows. Instead, they sit alongside them, requiring the long and costly rewriting of models if they even have a chance to train on different datasets.
These platforms aren’t built to reduce the technical load, integrate into existing stacks and workflows, or adhere to regulatory compliance, making them the wrong choice for seamless, scalable federated learning.
Choosing the right federated learning platform
It’s clear that the challenges organizations face when considering federated learning lie in the platform's suitability. These challenges are easily overcome with the right platform and the right partner can ensure that:
Your data will never move from your environment. Data collaboration must put you in full control of your data and offer strict definitions of who can access it and why. It’s important to find a federated learning platform that prioritizes privacy whilst delivering the maximum analytical value from the data sets. IP will be protected, privacy will be maintained, and regulatory requirements will be adhered to.
You don’t have to choose between privacy and flexibility. We’ve established that PETs are a must for federated learning platforms, yet not all platforms will allow you to pick and choose the best PET for your needs. Make sure to find a provider that isn’t tied to a single PET and can therefore work across different computations. By doing so, you can ensure greater flexibility and scalability across a range of use cases.
Your federated learning solution slots seamlessly into the way you work. Finally, you should choose a solution that can integrate with your established tools and machine learning workflows. With this in place, you will be able to leverage existing models, automate model training, and build data applications across organizations, geographies, and use cases with ease.