For uniform scalar quantization, the error distribution is approximately a uniform distribution
over an interval (which is also a 1-dimensional ball). Nevertheless, for lattice vector …

One-shot channel simulation (or channel synthesis) has seen increasing applications in
lossy compression, differential privacy and machine learning. In this setting, an encoder …

Channel simulation involves generating a sample Y from the conditional distribution P_Y|X,
where X is a remote realization sampled from P_X. This paper introduces a novel approach …

Federated learning (FL) offers a promising framework for obtaining a global model by
aggregating trained parameters from participating clients without transmitting their local …

In this work, we study the problem of communicating multiple samples from an unknown
probability distribution using as few bits as possible. This is a generalization of the channel …

Most existing work on Federated Learning (FL) transmits full-precision weights, which
contain a significant amount of redundant information, leading to a substantial …

To reduce the communication cost of differential privacy mechanisms, we introduce a novel
construction, called Poisson private representation (PPR), designed to compress and …

In image compression, with recent advances in generative modeling, the existence of a
trade-off between the rate and the perceptual quality (realism) has been brought to light …

We construct a randomized vector quantizer which has a smaller maximum error compared
to all known lattice quantizers with the same entropy for dimensions 5, 6,..., 48, and also has …