Federated learning is the most efficient technology out there that can help data scientists to build high-quality machine learning models in industries where data is extremely difficult or even impossible to obtain. These industries face many challenges, not only regarding sensitivity and data privacy but also internal restrictions with regards to protecting their own model IP.
It can be a daunting process when it comes to selecting a federated learning platform for data collaboration with multiple partners.
This is why we at Apheris have put together this article to make your selection process quick, easy, and painless!
If you want to dive deeper into the specific requirements for buying data collaboration platforms, we have got you covered: Our Buyer's Guide for Secure Multi-Partner Data Collaboration Platforms includes tons of valuable insights for your procurement process.
Federated Learning: A Quick primer
A federated learning platform is a solution designed for data science on distributed and therefore non-centralized data. Federated learning techniques allow different companies to use their data together to jointly train machine learning models without having them directly sharing their data or centralizing it. This has many advantages, as the picture below depicts:
As such, computation requests are sent to the different data providers, and machine learning models are trained locally on the data provider's data. Then, only the locally trained model parameters are shared between the parties - meaning the underlying training data stays under the control of the data providers. A federated learning platform facilitates setting up distributed model training workflows while providing many other features that can enhance privacy-preserving measures and make the process of collaborating more efficient.
Who Invented Federated Learning?
The term federated learning was first coined by Google in 2016 as part of their efforts to devise a decentralized method for using data from mobile devices to improve the user experience of other AI-based solutions. Previous applications of machine learning techniques required a centralized approach where data would be uploaded to a centralized server so that machine learning models could run on the data. Google’s work on decentralized approaches to machine learning stems from the company’s interest in the abundance of data that is available on mobile devices.
Federated learning is now being applied by multiple consortia and projects across different industries, such as pharmaceuticals, finance, manufacturing, and healthcare.
The Hidden Risks of Federated Learning
Federated learning was initially intended to reduce the risk of privacy violations in data sharing, specifically in response to emerging American federal frameworks and standards for data privacy protection.
However, federated learning as a methodology does not necessarily ensure that data privacy is preserved.
When handling real-life data with partners, there is much more to this. Platforms must employ additional methods of security and privacy-enhancing measures to guarantee that data and algorithmic assets are protected and enforced by all partners in a multi-partner data collaboration.
Alternatives to Federated Learning
While it is possible to share sensitive data, such as through anonymization or encrypted data sharing systems, these approaches either strongly limit the usefulness and value of the data or have limited scaling options.
In combination with privacy-preserving technologies and contractual and legal frameworks, federated learning is the only cutting-edge technique to truly enhance data collaboration and empower companies to exploit the commercial value of data with AI.
Yet, how can you be sure that you are picking the right federated learning platform for your data collaboration needs?
The process of identifying the right platform can be overwhelming, especially since federated learning is a new technology and the market for enterprise-grade platforms is relatively recent.
This is where our article comes in handy, as we walk you through the various features offered by federated learning platforms and outline the selection criteria for you to consider when searching for the right platform for your company’s needs.
The Future of Federated Learning
With this very young technology, one would think that it is not really worth talking about the "Future of Federated Learning" yet. At Apheris, we see this differently. Especially when implementing in an enterprise context, it is important to maximize the chances of success, to explore overlooked questions and to plan ahead. In our whitepaper "Beyond MLOps - How Secure Multi-Partner Data Collaboration Unlocks the Next Frontier of AI Innovation" we describe in detail what it really takes to securely and efficiently collaborate with partners and leverage the true potential of Federated Learning and other next-gen Privacy-enhancing Technologies already today.
The most important aspect to understand is that Federated Learning - or any other single Privacy-enhancing Technology, such as Differential Privacy or Synthetic Data alone are not sufficient. When working on actual use cases, it is more of orchestrating different PETs, combined with additional security layers and access controls. The implementation of such systems into enterprise environments is extremely complex. It requires expert knowledge in many different disciplines, such as data science, data engineering, IT architectures, compliance, and legal.
Platforms that offer federated and privacy-preserving data science work to address these complexities, foster innovation by driving consistency and governability between collaborating companies, and improve productivity with integrated workflows across data science tools, infrastructure, and privacy-enhancing technologies.
Learn everything about what it really takes to securely collaborate with partners on AI and sensitive data in our latest whitepaper.
Five Essential Selection Criteria to Find the Right Federated Learning Platform
Below, we guide you through five of the most important questions that you should consider:
Is it based on a secure and federated architecture?
How does it integrate into my existing IT and data science infrastructure?
Does it support a full data science workflow?
Is it easily scalable and future-proof?
Does it fulfill my companies' requirements regarding compliance, security, and IP protection?
Following these steps will ensure that you choose a federated learning platform matched to your goals and resources.
Secure and Federated Architecture
Federated learning systems are large-scale and distributed across multiple companies, which presents a lot of architectural design challenges. Most importantly, these challenges focus on communication and computation efficiency, data privacy, model performance, reliability, and system security. It is very important to get buy-in from all enterprise IT architecture and compliance teams. Otherwise, the projects will fail.
Let's take system security as an example.
This can be a huge issue due to the distributed ownership of a federated learning system. Client nodes are owned by different parties and might not be governed by the system owner. That makes it possible for malicious attackers to join the system, steal parameters, and reverse engineer the model. This can cause enormous damage to companies, considering the high costs of AI and ML - and last but not least, the irreparable loss of a company's reputation when made public.
Furthermore, adversarial clients may harm the model or system performance by uploading dishonest updates.
This is why all communication between different components in the federated learning platform must be encrypted at all times so that only computation results move between isolated and confidential environments.
At Apheris, our customers appreciate that they can maintain privacy and protect their IP in their multi-partner data collaborations without the hassle of working with overly complicated setups. Thus, we have designed the Apheris Platform to be built on a lean architecture, so complexity, privacy, and security risks don't get in the way of your data collaborations.
Flexible & Quick Deployment
Deploying a solution at one company can be a challenge. For an entire group of companies, it is a massive undertaking. Since risk and security teams within large enterprises have loads of tasks, deployment instructions and setup must be lean and simple.
In any case, you want to avoid anything that would make the deployment a stressful, high-stakes activity or last longer than it should. To ensure deployment is routine and uneventful, a federated learning platform needs to support secure, reliable, flexible, and simple deployment modes.
Your company may be operating in different regions where the legal requirements around data usage and storage will vary. Therefore, your chosen platform needs to have a high level of flexibility in its deployment options to cater to all of the participants entering the collaboration. Since deployment can span multiple partners with different architectures, there are very high demands on the clean and precise implementation of a solution.
Data Science Workflow Support
Data scientists can be picky folks. The best platform makes sure that it is intuitive to use and it seamlessly supports the workflow and tools that data scientists use.
Your company's data science workflow encompasses more than the application of machine learning models. Data from different providers can come in a variety of formats and often needs to be prepared ahead of time. These preparation processes, that occur before a model is applied to the data, are key parts of your data science workflow, and they help to ensure that the models will generate valuable insights into the data.
Because multiple types of data providers are needed to achieve robust federated learning outcomes, federated learning platforms need to be able to support an end-to-end data science workflow.
These platforms should have a great user experience for your data scientists and be able to be seamlessly adopted into their existing workflow. Platforms that support your company's end-to-end workflow should offer the following features:
Ease of use for data scientists - they need an intuitive way to build and run federated queries and models
End-to-end data science workflows - from preprocessing to statistical analysis and ML models
Ability to inspect remote data without leaking sensitive information
Because federated learning is layered on top of the existing IT infrastructure of your company’s data science pipeline, its integration capabilities can be limited if it is not adaptable to these pipelines and channels. In particular, if the platform is incapable of supporting your data science tools and languages, the platform cannot use your data to fully achieve meaningful insights during collaboration.
The ideal platform should be model- and data-agnostic. That means that federated machine learning processes can be applied to different distributions and types of data.
It also needs to be able to work with both vertically and horizontally distributed datasets. This allows you to gain maximum insights from your partners' complementary data while ensuring that the needs of all the participants are being met.
Because of this, we have created the Apheris Platform to be workable with all Python-based tools that span multi-partner data collaborations.
Our customers and their data scientist teams love it since it is easy to use and saves them unnecessary time and energy spent maintaining their own or learning new federated learning frameworks.
The Apheris Platform also offers preprocessing possibilities, secure and privacy-preserving data exploration tools, and overall flexibility to achieve the most valuable models in your collaborative data science projects.
Scalability
Federated learning platforms need to work for a growing number of participants and adjust to an increasing amount of complexity. One of the main issues with specific privacy-enhancing technologies, such as synthetic data or homomorphic encryption, is that they are not scalable.
Scalability is essential when considering your long-term reliability. It also needs to be future-proof with regards to emerging use cases, additional attack vectors, new privacy concerns, and more.
A scalable platform will have the following:
A platform setup that can theoretically allow for an indefinite number of data providers to connect to the centralized computation node
Provisions for a theoretically indefinite amount of computational power through its partnerships with processing capacity providers
System updates to the platform that regularly set out to confront emerging security threats and continue to fill potential gaps in the platform
Scalability in terms of complexity is also an essential feature with regards to mitigating privacy and security concerns. When dealing with other data collaboration methods, certain encryption techniques can result in issues related to being unable to scale if the arithmetic complexity is high.
As a result, these computations can end up requiring an impossibly high level of processing power. Working with a scalable solution can help you overcome the intrinsic problematic aspects tied to other methods of data collaboration.
When looking for a federated learning platform, you should try to find a long-term solution for your data collaboration needs. You don't want a platform that will only work for one specific use case.
At Apheris, we are constantly looking towards the future to provide you with the solution that will lead to sustainable collaborative practices. We are building an easily extendable platform that will grow with your needs and the changing landscape of scaling artificial intelligence across your industry.
Compliance, IP & Privacy-Preserving Measures, and Security
Trust in data collaboration is paramount, but waiting for the approval of legal departments from multiple companies can be tedious. Nonetheless, privacy, legal compliance, governance, and security are all important aspects to consider in your assessment. Companies seeking out federated learning platforms for their collaborations want to use a federated architecture because of its potential for private and secure multi-partner data collaborations that are legally compliant. However, platforms can have different approaches to dealing with these security and compliance issues.
It is important to note that federated learning on its own cannot protect the privacy and IP of your datasets.
The concept relies on a platform to enforce and implement privacy-preserving measures, as well as compliance, and governance frameworks when engaging in data collaborations.
As discussed in our section on flexible deployment, data collaborators can have different regional data sharing laws that they must comply with, and the federated learning platform they use needs to be flexible enough to accommodate different compliance concerns.
Additionally, governance methods need to be clearly established, as the rights and roles of partners in the collaboration should accommodate for insecurities created by any potential power imbalances among the collaborating participants.
Your chosen platform should help you build clear and strategic methods to foster trust and quell any fears of potential IP breaches and theft of trade secrets in your data collaborations. For more on the role, that power imbalances play in data collaborations, make sure to check out our other blog article.
Enterprise-grade federated learning platforms will have the following features for maintaining and preserving privacy and legal compliance, as well as implementing forms of governance:
| Feature | What's important |
|---|---|
| Compliance & Governance |
|
| IP protection & security |
|
| Privacy |
|
Your chosen platform needs to establish privacy through principles that encompass an end-to-end setup and advocate for the rights and needs of its users. These security aspects must both limit the ways that data can be read by your data scientists to uphold security while also being functional in enabling your data collaborations.
At Apheris, trust and confidence within multi-partner data collaborations are our top priority.
Not only do we provide contract blueprints to help you get started with your data collaboration journey, but we also offer support from our dedicated and experienced legal team to guide you through the process of ensuring compliance and negotiating legal frameworks.
What Federated Learning Platform Do You Need?
Choosing a platform for federated learning isn’t a quick process. You need to make sure that you are doing your due diligence to find the right solution based on your specific use cases and requirements.
However, the time and energy that you put into the process will be well worth it.
With the right federated learning platform, you’ll be able to rapidly launch new data collaborations with multiple partners, drive new revenue from your AI projects, and lead your industry for years ahead.
Plus your engineering, compliance, and legal teams will thank you for reducing their workload; they won’t have to spend time building and maintaining federated learning solutions or craft legal frameworks for months, or even years on their own.
