83 citations · 152 across the 4 of their papers we have counts for
4 papers · 1 filter
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik +3
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that co…
Active Federated Learning
Jack Goetz, Kshitiz Malik, Duc Bui +3
Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, the…
Federated User Representation Learning
Duc Bui, Kshitiz Malik, Jack Goetz +4
Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propo…