Large amounts of data are generated nowadays on edge devices, such as phones, tablets, and wearable devices. However, since data on such personal devices is highly sensitive, training ML models by sending the users’ local data to a centralized server clearly involves significant privacy risks. Hence, in order to enable intelligence for these privacy-critical applications, Federated Learning (FL) has become the de facto paradigm for training ML models on local devices without sending data to the cloud. Our research in this direction focuses on developing new FL approaches that exploit data and devices heterogeneity.
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