On Privacy and Personalization in Federated Learning: A Retrospective on the US/UK PETs Challenge
ML @ CMU
MAY 12, 2023
Unfortunately, while this data contains a wealth of useful information for disease forecasting, the data itself may be highly sensitive and stored in disparate locations (e.g., In this post we discuss our research on federated learning , which aims to tackle this challenge by performing decentralized learning across private data silos.
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