83 citations · 179 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Federated Learning via Synthetic Data
Jack Goetz, Ambuj Tewari
Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data. However the standard method is to transmit mode…
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…