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In the 2000s, the “cloud” began to take off. Programmers and companies began to procure virtual computing resources on demand to run their software and applications.
Over the last two decades, developers have grown accustomed to and rely on infrastructure that is readily available and managed and maintained by someone else. And that’s no surprise. The abstraction of hardware and infrastructure allows developers and companies to focus primarily on product innovation and user capabilities.
Amazon Web Services, Microsoft Azure, and Google Cloud have made storage and compute ubiquitous, on-demand, and easy to provision. And these hyperscalers have built resilient, high-margin companies on that approach. Enterprises that rely on the cloud have traded capital expenditures (servers and hardware) for operational expenditures (pay-as-you-go compute and storage resources).
Enter federated learning
While the ease of use of the cloud is a boon to any aspiring team trying to innovate at all costs, a cloud-centric architecture is a significant cost as a business grows. In fact, 50% of the revenue of large SaaS companies goes to cloud infrastructure.
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As machine learning (ML) becomes more popular and useful, organizations are storing more data in the cloud and training larger models in search of higher model accuracy and greater user value. This further increases dependency on cloud providers and organizations find it difficult to offload workloads to on-premises solutions. In fact, they would need to hire an excellent infrastructure team and completely redesign their systems.
Organizations are looking for tools that enable new product innovation and provide high accuracy with low latency while being cost effective.
Enter Federated Learning (FL) at the Edge.
What is Federated Learning (FL) at the Edge?
FL, or Collaborative Learning, takes a different approach to data storage and computation. For example, while popular cloud-centric ML approaches send data from your phone to central servers and aggregate that data in a silo, FL on the Edge keeps the data on the device (i.e. your phone or your tablet). It works in the following way:
Step 1: Your Edge device (or mobile phone) downloads a base model from a FL server.
Step 2: The on-device training is then carried out; On-device data improves the model.
Step 3: The encrypted training results are sent back to the server for model improvement, while the underlying data is stored securely on the user’s device.
Step 4: With the model on the device, perform fully distributed and decentralized training and inference on the edge.
This loop continues iteratively and your model accuracy increases.
Benefits of federated learning for the user
When you don’t rely on data centralization or have a bottleneck, the user benefits in dramatic ways. With FL on the Edge, developers can improve latency, reduce network calls, and increase power efficiency while promoting user privacy and improving model accuracy.
FL on the Edge is made possible by the ever increasing hardware power of the phones in our pockets. Every year the on-device calculation and the battery life improve. As the smartphone processor and hardware in our pockets improve, FL techniques will unlock increasingly complex and personalized use cases.
For example, imagine software that sits on your phone in a privacy-centric manner and automatically composes replies to incoming emails with your custom tone of voice, punctuation style, slang, and other hyper-personalized attributes—all you have to do is click “Send.” “ to click.
Enterprise pull is strong
In my conversations with several Fortune 500 companies, it became clear how strong the cross-industry demand for FL at the edge is. CTOs express how they searched for a solution to bring FL techniques to life on the edge. CFOs point to the millions of dollars spent on infrastructure and model deployment that could otherwise be saved with an FL approach.
In my opinion, finance, media and e-commerce are the three industries that have the greatest potential to benefit from federated learning. Let me explain why.
Use Case #1: Finance – improved latency and security
Many large multinational financial companies (Mastercard, PayPal) are looking to adopt FL on the Edge to help them identify account takeovers, money laundering and fraud detection. More accurate models are on the shelf and have not been released for market launch.
Why? These models increase latency just enough to negatively impact user experience — we can all think of apps we no longer use because they took too long to open or crashed. Businesses cannot afford to lose users for these reasons.
Instead, they accept a higher false-negative rate and suffer excessive account hijacking, money laundering, and fraud. FL on the Edge enables organizations to simultaneously improve latency while demonstrating a relative increase in model performance compared to traditional cloud-centric deployments.
In the media sector, companies like Netflix and YouTube want to increase the relevance of their suggestions for films or videos to watch. The Netflix prize famously awarded $1 million for a 10% performance increase over its own algorithm.
FL on the edge has the potential to create a similar effect. When a new show launches today or a popular sporting event is live (like the Superbowl), companies reduce the signals they receive from their users.
Otherwise, the sheer volume of data (at a rate of millions of requests per second) creates a network bottleneck that prevents them from recommending content at scale. With edge computing, businesses can use these signals to suggest personalized content based on insights from individual users’ tastes and preferences.
Use Case #3: eCommerce – more timely and relevant suggestions
Finally, ecommerce and marketplace businesses want to increase click-through rates (CTR) and increase conversions based on real-time feature stores. This allows them to re-rank recommendations for customers and make more accurate predictions without the lag of traditional cloud-based recommendations.
For example, imagine opening the Target app on your phone and receiving highly personalized recommendations for products in a completely privacy-focused manner – no identifying data would have left your phone. Federated learning can increase CTR thanks to a more powerful, privacy-aware model that provides users with more timely and relevant suggestions.
The market landscape
Thanks to advances in technology, big companies and startups alike are working to make FL more ubiquitous to benefit businesses and consumers alike. For businesses, this likely means lower costs; for consumers, it can mean a better user experience.
There are already some early players in this space: Amazon SageMaker allows developers to deploy ML models mainly on edge devices and embedded systems; Google Distributed Cloud Expands Its Infrastructure to the Edge; and emerging companies Nimbleedge are reinventing the infrastructure stack.
While we’re in the early innings, FL is on the fringes and the Hyperscalers are in an established dilemma. The revenues that cloud providers generate for computing power, storage and data are at risk; Modern vendors that have adopted an edge computing architecture can offer customers best-in-class ML model accuracy and reduced latency. This improves the user experience and increases profitability – a value proposition you cannot ignore for long.
Neeraj Hablani is a Partner at Neotribe Ventures and focuses on early-stage companies developing breakthrough technologies.
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