Federated learning: The killer use case for generative AI

Let’s imagine a fictional company, Global Retail Corporation, a multinational retail chain struggling with its initial approach to AI integration. They built custom generative AI applications on their cloud provider using OpenAI’s APIs for broader analysis, providing access to their LLMs (large language models) and ChatGPT to get more strategic and valuable answers to their business questions. The process was costly and complex, and it delivered suboptimal results. That all changed when they adopted federated learning.

The strategy of federation in AI deployments

Federated learning is emerging as a game-changing approach for enterprises looking to leverage the power of LLMs while maintaining data privacy and security. Rather than moving sensitive data to LLM providers or building isolated small language models (SLMs), federated learning enables organizations to train LLMs using their private data where it resides. Everyone who worries about moving private enterprise data to a public space, such as uploading it to an LLM, can continue to have “private data.” Private data may exist on a public cloud provider or in your data center.

The real power of federation comes from the tight integration between private enterprise data and sophisticated LLM capabilities. This integration allows companies to leverage their proprietary information and broader knowledge in models like GPT-4 or Google Gemini without compromising security. More importantly, it means not having to deal with moving petabytes of data to a public cloud that’s also hosting an LLM.

For our fictional company, their customer transaction data, inventory systems, and supply chain information could contribute to training advanced language models while remaining within their secure cloud environment. They leverage the data where it exists, cloud or no cloud, and thus, there is no need to move the data to another cloud provider or even to another space within their public cloud provider. The resulting system provides more profound insights and accurate predictions than building standalone AI applications.

Financial and operational advantages

The federated approach offers significant cost advantages. Organizations can leverage existing cloud resources where their data already resides rather than maintaining separate AI infrastructure and paying for extensive data transfers.

Recent developments have made federated learning more accessible. New frameworks enable seamless integration between edge-based SLMs and cloud-based LLMs, creating a hybrid architecture that maximizes benefits while minimizing risks. This approach is particularly valuable for organizations dealing with sensitive data or needing to comply with regulations, but mainly, it’s just architecturally simpler and thus easier and faster to build and deploy.

As a generative AI/cloud architect, I’ve found that the core issue in designing and deploying these beasts is their innate complexity, which is unavoidable as you add many moving parts, such as replicating your business data for training data for an LLM. More complexity means more cost, worse security, and enterprises being architecturally lazy overall.

The next generation of enterprise AI architecture

As enterprises struggle to balance AI capabilities against data privacy concerns, federated learning provides the best of both worlds. Also, it allows for a choice of LLMs. You can leverage LLMs that are not a current part of your ecosystem but may be a better fit for your specific application. For instance, LLMs that focus on specific verticals are becoming more popular. However, they are typically hosted by another provider.

The future of enterprise AI lies not in isolated solutions or purely cloud-based approaches but in federated systems that combine both strengths. Organizations that embrace a federated approach will find themselves better positioned to extract value from their data while maintaining required levels of security and compliance.

For companies like Global Retail Corp., the switch to federated learning isn’t just about technology, it’s about finding a more efficient, secure, and effective way to harness the power of AI. As more enterprises face similar challenges, federated learning is poised to become the standard approach for implementing generative AI in the enterprise (according to me). Given the architectural and cost advantages of using these mechanisms to couple your enterprise’s data with a public LLM’s vast knowledge, I’m not sure why it’s not a bigger deal. It’s the easiest way.

A practical road map for federated learning

The path to federated learning begins with thoroughly understanding your current data landscape. Start by conducting a comprehensive assessment of where your data lives, how it’s governed, and how it flows through your organization. This foundation will reveal the potential integration points for federated learning systems and highlight gaps in your infrastructure.

The technical groundwork requires careful attention to detail. Your organization needs standardized data labeling practices, robust edge computing capabilities where necessary, and reliable network connectivity between data sources. Create testing environments that accurately reflect your production data distribution.

Organizational readiness is equally important. Create teams that bring together data scientists, security experts, and domain specialists. These cross-functional groups should work together to establish governance frameworks and metrics for success. Don’t forget to create clear data-sharing agreements between departments. These will be essential for federated learning to work effectively.

When you’re ready to begin implementation, start small. Identify contained use cases that can serve as pilots and carefully select technology partners that understand your specific needs. Define clear success criteria for these initial projects and establish robust monitoring frameworks to track progress.

Remember, the goal isn’t to rush into complex federated learning systems but to build a solid foundation to support your enterprise’s future AI. A measured approach focusing on infrastructure, skills, and organization will position you for success as federated learning technology evolves and matures.

Federated learning represents a strategic evolution for enterprises looking to harness the full power of their data and use it where it exists. As federated learning continues to gain traction, companies that adopt a thoughtful strategy will be better equipped to unlock deeper insights and drive meaningful business outcomes.

The future belongs to those willing to embrace this paradigm shift and leverage emerging technologies to their full potential. Will you be a part of it?