2 citations · 3 across the 21 of their papers we have counts for
6 papers · 1 filter
Model Segmentation for Storage Efficient Private Federated Learning with Top Sparsification
Sajani Vithana, Sennur Ulukus
In federated learning (FL) with top sparsification, millions of users collectively train a machine learning (ML) model locally, using their personal data by only communicating…
Rate-Privacy-Storage Tradeoff in Federated Learning with Top Sparsification
Sajani Vithana, Sennur Ulukus
We investigate the trade-off between rate, privacy and storage in federated learning (FL) with top sparsification, where the users and the servers in the FL system only share t…
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) in relation to private federated submodel learning (FSL), where a machine learning model is divided into multiple sub…
Rate Distortion Tradeoff in Private Read Update Write in Federated Submodel Learning
Sajani Vithana, Sennur Ulukus
We investigate the rate distortion tradeoff in private read update write (PRUW) in relation to federated submodel learning (FSL). In FSL a machine learning (ML) model is divided in…
Private Federated Submodel Learning with Sparsification
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification. In FSL, a machine learning model is divided into multiple s…
Private Read Update Write (PRUW) with Storage Constrained Databases
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) in relation to federated submodel learning (FSL) with storage constrained databases. In PRUW, a user privately reads…