We've looked at this approach in the past and I expect we'll see much more of it. "An approach called federated learning trains machine learning models on devices like smartphones and laptops, rather than requiring the transfer of private data to central servers." Typically the edge devices (ie., your smartphones and laptops) will install an already partially trained model (say, something that already knows about musical genres) and then use your own data to complete the training (say, your own musical selections). Edge AI can also feed generalized results (not personal data) back to the central server. "Federated learning relies on an algorithm that serves as a centralized coordinator. It delivers the model to the devices, trains it locally on the relevant user data, and then brings each partially trained model back and uses them to generate a final global model."
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