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Funded by Legacy Global Consulting, Inc.

Federated Learning Vs. Cloud On Privacy, Speed, And Cost – Brian C Jensen

Brian C Jensen

Businesses today are taking a closer look at artificial intelligence (AI) solutions for their operations, and yet the biggest hurdle remains—how do you balance privacy, speed, and cost in a way that works best for your business? With cloud computing costs rising, many businesses have begun to turn to alternative edge AI solutions. One of these is Federated Learning (FL), an emerging technology that offers a secure alternative to cloud-based algorithms while also boasting more efficient processing speeds and lower operational costs. In this blog post, Brian C Jensen takes a deep dive into how FL can offer unparalleled privacy protections compared to its cloud-based counterparts as well as why it could be the go-to choice when weighed against other edge AI solutions.

Federated Learning At The Edge May Out-Compete The Cloud On Privacy, Speed, And Cost, Says Brian C Jensen

Federated learning at the edge is rapidly becoming an increasingly popular alternative to cloud-based machine learning, says Brian C Jensen. In Federated Learning, data stays where it’s generated and never leaves its source device – such as a mobile phone or IoT device. This makes Federated Learning much more appealing from a privacy perspective than its cloud-based counterpart; the data remains secure on devices rather than being sent out to centralized servers, mitigating the risk of a breach or compromise.

Furthermore, Federated Learning can offer significantly better performance in terms of latency due to the fact that data does not need to traverse multiple networks before reaching the model training environment. Instead, Federated Learning distributes models across many participating devices which have direct access to their local datasets, enabling fast, real-time training. According to Brian C Jensen, this makes Federated Learning ideal for applications requiring low latency, such as autonomous vehicles and virtual reality.

Finally, Federated Learning can also be much more cost-effective than cloud-based machine learning due to the fact that it does not require costly infrastructure or energy consumption. In fact, a recent study conducted by Google revealed that Federated Learning could reduce model training costs by up to 90%, making it a much more attractive option in terms of economics.

To illustrate the impact Federated Learning at the edge has on privacy, speed, and cost over its cloud counterpart, let’s consider an example from healthcare. A hospital may want to develop an AI-powered system capable of predicting instances of heart disease with high accuracy, but due to the sensitive nature of patient data, it wants to ensure that the data remains on-premises. Federated Learning could enable them to develop and utilize this AI system without needing to send any data out of the hospital — reducing privacy risks whilst also providing quick real-time results at a significantly lower cost than cloud-based training.

Brian C Jensen’s Concluding Thoughts

Overall Federated Learning is beginning to surpass its cloud counterpart in terms of privacy, speed, and cost and has become an increasingly attractive alternative for organizations looking to leverage machine learning algorithms. According to Brian C Jensen, with advances in technology such as 5G networks Federated Learning will only become more popular over time – allowing us to reap its many benefits while maintaining our data privacy.