It’s no secret that there are incredible opportunities to be seized through federated machine learning. From manufacturing to marketing, healthcare to consumer technology, organizations that have injected some much-needed machine learning into their data science operations have been rewarded with improvements in reliability, efficiency, and more. This has enabled life-saving drugs to be developed, manufacturing processes to be optimized, and customer experiences to be enhanced across a range of industries.
But, while some early innovators have recognized the collaborative potential of federated machine learning, many have yet to seize the opportunity before them. The biggest barrier to doing so is the perceived tricky nature of data collaboration; many organizations believe that to train their machine learning models with datasets beyond their own, they must share their data with other parties. Doing so would require implicit trust as they relinquish control of their data to an outside party and accept the risks that come with it.
Such a process is, understandably, a line most organizations refuse to cross. They want the benefits that come with data collaboration but don’t want the associated risks of sharing their data, which could impact their regulatory compliance, offer competitors insights into valuable intellectual property, or see that data used for purposes beyond what was originally agreed.
Those that have attempted to share data in this way have been forced to create overly complex data sharing agreements that occasionally work for single-use cases but frequently fail when it comes to scaling or automating beyond that case.
The case for federated learning
It is because of the intricate nature of data sharing and the reluctance to trust a third party completely that so many organizations have yet to truly seize the machine learning opportunity before them. But there is a simpler way: federated learning.
Federated learning is not a new concept, but it is one that many have failed to recognize. Google has been utilizing federated learning since 2016, using it to simultaneously protect user data while improving user recommendations. Without federated learning, none of us would be able to say “Hey Google” next time we’re looking for help from Google Assistant.
Google, and many organizations since, have proved that by implementing a federated infrastructure, data collaboration can be achieved without any party needing to move, risk, or centralize their precious data. The result? All of the rewards of data collaboration and none of the risk that comes with data sharing.
Most recently, Microsoft recognized the potential of machine learning-ready data when it launched its 10-year partnership with the London Stock Exchange Group (LSEG), a partnership that has granted Microsoft access to machine learning-ready datasets comprising its own and those across the LSEG. Microsoft, and group members who will also receive access through the partnership, will use the newly available data it will use to develop innovative new solutions to problems facing the global financial industry today.
Both organizations have achieved this through a federated MLOps framework that enables them to work with countless third parties, partners, and datasets, accessing their sensitive data to drive their artificial intelligence and machine learning models. Such a framework removes the complexities that come with each party choosing its preferred frameworks – which inevitably become more challenging to maintain as more and more partners enter the ecosystem – and instead ensures secure collaboration between multiple parties at scale.
Collaboration-driven machine learning in action
With so much data being generated every day, across every industry, the potential of using data collaboration to improve machine learning models is huge. Here are just some examples of how industries could use such an approach to radically improve their operations:
Manufacturing: Data is generated at every step of the production line. Collaborating on data from raw materials, the mining process, the composition of batteries and materials, and even the recycling process could not only improve efficiencies in the production line, but it could also have huge impacts when it comes to sustainable production.
Healthcare and pharma: Breaking down silos and enabling collaboration on sensitive patient data – from MRI scans to drug efficacy – could help drive clinical trials, improve the detection of diseases and expand the life expectancy in the face of today’s most debilitating illnesses.
Financial services: Federated learning could help banks and financial services organizations derive smarter insights from their customer’s financial records, allowing them to better understand economic challenges and individual customers’ behavior, and improve their ability to detect increasingly sophisticated fraud.
Consumer technology: Just as Google has harnessed federated learning to improve its voice commands and the usability of its Android Keyboard, so too has Apple used it to improve Siri’s voice recognition capabilities. Technology should make all our lives easier, after all, and the more companies that follow in the footsteps of Apple and Google, the better the everyday digital experience of consumers worldwide.
It’s clear that data collaboration possibilities are endless, and that the benefits for all of us could be life changing. It’s also clear that the barrier to collaboration-driven machine learning is not industry-specific but rather a technical challenge that needs to be overcome across the board.
Federated learning is the key to overcoming that barrier and enabling better collaboration with partners on AI and sensitive data across industries. For more information on what it takes to implement federated learning into enterprise MLOps pipelines, head over to our whitepaper.
