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The Problem Nobody Talks About in AI Collaboration
All companies want smarter AI. Hospitals want models that can diagnose illness earlier. Banks want fraud detection that identifies suspicious patterns across institutions. Retailers want recommendations that truly seem personalized.
But there‘s a wall nobody wants to discuss: data cannot leave the building.
Rules, contracts, competitive issues the most you can do with the data being exchanged between the company is to actually use it. How do you train a model on data you can‘t even access?
Precisely that gap which federated learning and multi-party computation are comfortably filling and disrupting in subtle ways that, allow me to point out, are transforming the application of AI at scale.
What Federated Learning Actually Does (Without the Textbook Definition)
Here‘s another way of putting it: Suppose a few dozen hospitals all have records on patients and you‘re trying to learn from all of their data. How do you handle that? Well, traditionally, you‘d combine all the data into one megastorage system. Not gonna work. This leads to a ton of data compliance problems and excessive breach risk.
And this is where federated learning turns this on its head. It simply take the model to the data, rather than the data to the model.
A local copy is trained on data at each hospital. Only model updates i.e. gradients i.e. what the model has learned gets sent back to the base. The data record itself, never leaves.
I‘ve read this concept discussed abstractly dozens of times, but for the first time I felt its true significance when I saw a team of healthcare AI researchers describe how they are capable of effectively training models on data distributed across six hospital networks without a single patient‘s record crossing a physical institutional boundary. That‘s no small feat.
But: as you update the model, you might reveal some information about the training data (if you‘re not careful). That‘s where the second piece comes in.
Multi-Party Computation The Part Most Articles Skip
Multi-party computation (MPC) predates federated learning, and frankly, it doesn‘t get the recognition it deserves.
Main concept: Several people computing a function using their combined inputs without any one of them knowing the other‘s input. The classic example is called Yao‘s Millionaries Problem, where two people want to find out who‘s richer but don‘t want to tell the other their total net worth. MPC can solve this mathematically.
Signaling for AI: individual institutions contribute to a collective calculation of model updates, perform an aggregation, or verify the output without revealing individual values. Combined with federated learning, it seals the breach opened up by the sharing of raw gradients.
How These Two Technologies Work Together
Federated learning solves the training distribution problem – the data stays local. MPC tackles the aggregation trust problem – even the aggregation step should not reveal too much.
In practice, this means:
- Models are encrypted or secret shared by each user after updates are completed.
- The aggregation server combines them without decrypting each contribution.
- It‘s not that only the final; an aggregate number is reported back; not each and every detail.
This is most useful in situations where no one should see the whole picture (e.g. cross-bank fraud detection, or retail MI without revealing sales data).
Federated Learning and Multi-Party Computation in the Real World
This is no longer research territory, though.
Medical Applications: Google health and DeepMind have tried federated method for medical image. They want to train diagnostic models across hospital systems in the different countries, where they have distinct data protection regulation.
Mobile phones: Google‘s Gboard keyboard applies federated learning as a way to improve next-word predictions without sending any typing data off the phone. I used Gboard for several years and witnessed a consistent enhancement of autocorrect while making my own contribution with every keystroke I didn‘t even send.
Finance: banks leverage federated and MPC-based systems in developing joint fraud schemes. Banks share in pattern detection but customer transaction histories are not made available to other Participants.
Cross-border AI: The requirements that companies in the EU and the US face around data residency differ. Federated learning can allow them to train the same models without moving data across jurisdiction.
Where Privacy-Enhancing Technologies (PETs) Fit Into This
Apparently, federated learning and MIGHT not just work alone. They along with many other tools are under umbrella called PETs (Privacy-Enhancing Technologies) which aims at data minimization while providing computation.
PETs include:
- Differential privacy perturbed output with tuned ratio, so individual records cannot be reverse calculated
- Secure enclaves a hardware level protected, separated domain for executing sensitive information.
- Homomorphic encryption how we compute directly on the encrypted data.
- MPC computation involving several parties without revealing inputs to each other .
- Distributed model training: federated learning
The structure of federated learning and MPC is ‘privacy-preserving’ privacy is part of how the computation is set up to happen. It is not something bolted on to the side.
I observed in my research that many organizations view differential privacy and give more institutions as a secondary aspect of federated systems which makes sense the two capabilities build on each other‘s safeguards.
The benefits of Homomorphic Encryption and why it is both promising and painful
Homomorphic encryption refers to the method where you can perform computational work on data while it is encrypted and the results, once decrypted, would be as if you performed the computations.
This sounds like a silver bullet for collaborative AI. And in principle, it is.
In practice? It‘s expensive. Machine learning on fully homomorphic encrypted data can be order-of-magnitude more computationally expensive compared to plaintext data. For simpler aggregations/operations (like Partial or somewhat homomorphic schemes), the overhead is easier to bear and you see it used in specific portions of FL pipelines, rather than entirely.
However, the technology is moving rapidly. We have seen a dramatic reduction in the gap over the last 3 years through hardware acceleration and algorithmic developments. IBM and Zama are continually extending that hardware boundary.
My Take on Where Homomorphic Encryption Realistically Fits Today
Right now, it‘s most practical for:
- Secure aggregation in federated systems (not in full training)
- Encrypted inference where a client encrypt an input and the server returns an encrypted prediction.
- Certain financial calculations where precision takes precedence over speed
Would this be suitable for training large models? Still a few years away from being feasible without hefty hardware investment.
PETs for Regulatory Compliance – Where Things Get Commercially Real
If you just want to build AI products that involve your user data – wherever in the EU, UK, or anywhere else that has serious data protection regimes in place – PETs for Regulatory Compliance is no longer thinking about optional.
GDPR starts by requiring data minimisation. As a result, HIPAA restricts what can exit its covered systems. Financial regulators in various jurisdictions have placed restrictions on how cross-border data flows. The UK Information Commissioner‘s Office has provided guidance on how PETs can be used to satisfy compliance requirements.
2. Data minimization is also met in directly with federated learning, where just model updates are shared and not records. MPC is also used to meet access control and need-to-know requirements. They can therefore give a compliance team something tangible to reference.
What I find interesting in practice is that once legal teams understand federated architectures, they tend to love them as the data governance story is a lot cleaner. Instead of having to negotiate complicated data sharing agreements, you‘re negotiating model sharing agreements, which are a lot less regulatory burdensome.
This is one of the less-publicized commercial benefits: federated + MPC systems can help speed up deals that would otherwise be held up at the Legal Review stage.
What Most People Get Wrong About These Technologies
A few misconceptions worth clearing up:
“Federated learning means your data is private.” Not necessarily. Without safeguards like secure aggregation, members inference attacks can reverse-engineer training data information from gradient updates. Differential privacy or above is required.
“MPC is just for cryptographers.” The math is complex, and the tooling has matured. Libraries such as PySyft, OpenMined, and FATE make federated + MPC configurations even available to ML engineers that do not have a cryptography PhD.
This is relevant just for giant companies.” Medium-sized companies with sensitive B2B data especially legal tech, HR analytics, supply chain have exceptionally tangible use-cases. Any situation where two organizations would like to leverage learnings from other‘s data without sharing sensitive information.
“Way too slow to be practical.” For certain applications, yes. For others – particularly asynchronous training, inference-only applications, and aggregation applications – the overhead on today‘s hardware is acceptable.
Who Should Actually Be Paying Attention to This
If you‘re a developer working on data products that involve regulated industries, this should be on your mind.
If you’re a product manager or technical founder, the pitch is straightforward: Federated learning and MPC give you something no one else can copy, a reasonable way to collaborate in the presence of sensitive data. A compelling value proposition for a healthcare, financial, or legal SaaS company or any B2B column.
If you are simply technically curious, this architecture is incredibly cool.It‘s a totally different paradigm that you can train a model on siloed, distributed data without having to gather the data itself.
Conclusion: The Honest Take
Federated learning and MPC aren‘t without their issues, either. They do complicate the problem, and they have to be implemented correctly, and in some use cases there still may be performance constraints for example: where end-to-end homomorphic encryption is sought.
But they do point to something truly valuable: a way to a collaborative AI that doesn‘t put your compliance stance at risk over a data sharing deal.
The technology stack of PETs, lean federated architecture and selective application of MPC already show results in the show floor systems over healthcare, finance and mobility. The tooling is maturing. The regulatory pull to go privacy-by-design is only heading in one direction.
In the 18–35 tech community building, or working around, AI systems as an engineer this is good to understand now, not later. Those who learn how to coordinate over data without sharing it are going to have a structural advantage over teams who can‘t get there yet.
I’m a technology writer with a passion for AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and curious. and encourage participation I continue to research innovation and technology. Let’s connect and talk technology!
