Meet with team smartData

Calendar April 17 - 21, 2023
location Chicago, IL

Federated learning is a machine learning technique that involves training an algorithm using numerous decentralized edge devices or servers that contain local data samples but do not share them. This strategy contrasts with traditional centralized machine learning methods, which necessitate the transfer of all local datasets to a single server. It can be employed in a variety of industries, including data protection, telecommunications, IoMT, and pharmaceutics.

Edge ML is a technique that enables Smart Devices to process data locally (through local servers or at the device level) utilizing machine and deep learning algorithms, decreasing reliance on Cloud networks. Edge devices continue to send data to the Cloud as necessary, but the ability to process certain data locally enables for screening of data sent to the Cloud while also allowing for real-time data processing (and response).

In healthcare, federated learning is being used to analyze patient-protected health information and remotely monitored data from devices or in local servers, in order to collaboratively learn a shared prediction model while keeping all training data secured. Resulting in the detection of care gaps, revenue leakage, and streamlining overall workflow. Decoupling the ability to do machine learning from the need to store the data in the cloud.

With the rise of IoMT came an explosion of Smart Devices connected to the Cloud/EMR, but the network was not yet ready to support this surge in demand. Federated learning provides a technique to recognize patterns from the massive data and use algorithms to predict the future outcomes of the patients, that are being used management many chronic illnesses.

Federated Learning allows for smarter models, lower latency, and less power consumption, all while ensuring privacy. It is having a significant impact on medical research, software as a medicine, and clinical trials.

In addition to providing an update to the shared model, the improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone. To know more please visit

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