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Found 6 papers, 2 papers with code
Recovering Labels from Local Updates in Federated Learning
Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients' data from communicated model updates.
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precision quantization schemes.
Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult.
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
In particular, each client extracts and sends to the server the means of local data representations and the corresponding soft predictions -- information that we refer to as ``hyper-knowledge".
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
A major challenge in federated learning arises when the local data is heterogeneous -- the setting in which performance of the learned global model may deteriorate significantly compared to the scenario where the data is identically distributed across the clients.
Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs
We propose Skeleton-Graph, a deep spatio-temporal graph CNN model that predicts the future 3D skeleton poses in a single pass from the 2D ones.
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