Personalized Federated Learning (FL) is an emerging research field in FL that learns easily adaptable global model the presence of data heterogeneity among clients. However, one main challenges for personalized heavy reliance on clients’ computing resources to calculate higher-order gradients since client segregated from server ensure privacy. To resolve this, we focus a problem setting where m...

Motivated by an open problem and a conjecture, this work studies the of single server private information retrieval with coded side (PIR-PCSI) that was recently introduced Heidarzadeh et al. The goal PIR-PCSI is to allow user efficiently retrieve desired message ...

We consider the design of lattice vector quantizers for the problem of coding Gaussian sources with uncoded side information available only at the decoder. The design of such quantizers can be reduced to the problem of nding an appropriate sublattice of a given lattice codebook. We study the performance of the resulting quantizers in the limit as the encoding rate becomes high, and we evaluate ...

Product and content personalization is now ubiquitous in e-commerce. There is typically too little available transactional data for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We formalize this problem as one of recovering a large-scale matrix, with side information in the form ...