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Found 3 papers, 1 papers with code
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Federated learning (FL) is a paradigm that allows several client devices and a server to collaboratively train a global model, by exchanging only model updates, without the devices sharing their local training data.
Location Leakage in Federated Signal Maps
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices.
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance.
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