The challenge for solution providers who work with AI is to develop robust applications that deliver valuable insights while keeping the enterprise and its users secure. Yet, while AI has the power to enhance security for applications and their data, older, centralized AI architectures ironically can create vulnerabilities. That's why more and more providers are turning to federated learning architectures and advanced hardware to prevent attacks. Based on the Intel eBook, Federated Learning for AI Analytics, we dive into federated learning and how it is poised to be the leading edge of AI analytics.
The challenge of centralized AI
When looking at two of the most private data sources, financial and medical, maintaining security is of utmost importance. Yet, by demanding that data is sent in its original form to centralized servers, traditional AI infrastructure creates multiple opportunities for privacy violations.
In addition, storing all individual user data (like health or financial records and names) in one location creates the ideal target for hackers. That's because with easy access to things like an individual's name, account information, and social security number, it's possible to steal identities, open accounts, and commit many other crimes.
What's worse, the algorithm processing the data can be exposed and manipulated with centralized AI, causing it to generate insights and make decisions detrimental to an organization, its people, and customers. These breaches can result in catastrophic situations.
Federated learning architectures - how they're different
Federated learning is an approach that changes the AI training paradigm. Instead of being collected and fed into one central server or site for processing, data is collected and processed by edge servers. This process enables more data collection from newer data sources like IoT and mobile devices.
Each edge-based AI server then strips away personal identifying information from the data and ingests it into a locally stored algorithm image. Only then will it relay that information to a centralized AI server farm that updates the algorithm and shares it with their entire organization. The net result is that personal user data is not exposed, reducing vulnerability to attack.
Federated learning - key benefits
To anyone designing, supporting, and securing AI-enhanced solutions, a federated learning architecture provides the following benefits:
Retained data sovereignty - the data collected by the enterprise remains with its owner.
Larger datasets without sharing - If there's one thing an AI can benefit from, it's a larger supply of secure data. Federated learning enables more data to be collected while retaining the anonymity of its source.
Topology flexibility - Federated learning architecture is flexible, allowing for combining nodes if needed.
PET enhanced security - Using homomorphic encryption, applications can encrypt medical and financial information to prevent exposure to unwanted servers.
Greater collaboration - By leveraging edge computing, data can be collected from more expansive geography allowing for richer, larger AI data pools.
A key ingredient to federated learning success - Hardware
As powerful as today's applications are, any edge-based solution is also dependent on hardware - especially when securing AI-enabled applications. In essence, any AI is more secure when both software and hardware work to secure it. One such example is the security inherent in Intel's latest CPUs.
Intel Software Guard Extensions (SGX)
Intel Software Guard Extensions are instructions that run on Intel CPUs. They protect code against snooping and malware attacks.
As a result, critical applications and their data are safe even if the OS, drivers, BIOS, VMM, and SMM are compromised. It's like having security that continues to fight back even after an intruder has access to the server.
In addition, SGX also prevents attacks on memory content like memory bus snooping, memory tampering, and cold boot attacks. So data can remain secure, no matter where on the edge it resides.
Finally, SGX also provides hardware attestation to verify the code itself and authorize data signatures. Therefore, the hardware itself can reject malicious code when needed.
Intel SGX benefits
Leveraging Intel SGX, solution providers are free to leverage federated learning to its fullest extent. And with their code protected from attack, developers can create trusted execution environments.
Overall, they can minimize the attack surface of the entire AI while remaining free to develop better-performing AI-based solutions.
How to leverage federated learning and Intel SGX today
Given the inherent advantages, more and more solution providers agree that edge-based, federated environments are the way of the future. A pivotal component to federated learning's design is hardware, strategically placed on the edge, operating at top performance, and secure. For that, software experts are served best by experts in hardware like UNICOM Engineering.
As an Intel Technology Provider, UNICOM Engineering has helped drive the latest solutions with our partners for decades. Our skilled team actively builds the latest solutions based on 3rd Gen Intel Xeon Platinum/Gold/Silver series processors, Intel Optane Persistent Memory 200 Series, Intel SmartNIC, and Ethernet 800 Series Network Adapters, and Intel Optane SSDs. Our customers benefit from solutions optimized for telecom, cloud, enterprise, network, security, IoT, and HPC workloads with expanded I/O, storage, and network connectivity options by leveraging our services. Learn more about how UNICOM Engineering can help you transition to next-gen solutions by scheduling a consultation.