We present two novel federated learning (FL) schemes that mitigate the effect of straggling
devices by introducing redundancy on the devices' data across the network. Compared to …

We consider a mobile edge computing scenario where a number of devices want to perform
a linear inference on some local data given a network-side matrix. The computation is …

We consider the problem of designing a coding scheme that allows both sparsity and
privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy …

Luby transform (LT) code has attracted a significant attention due to its rateless property and
low decoding complexity in distributed computing networks. However, for wireless networks …

We present a novel coded federated learning (FL) scheme for linear regression that
mitigates the effect of straggling devices while retaining the privacy level of conventional FL …

The ever-increasing scale and more stringent latency requirements of mobile computing
tasks have driven the recent development of distributed edge computing. In distributed edge …

This work investigates the design of sparse secret sharing schemes that encode a sparse
private matrix into sparse shares. This investigation is motivated by distributed computing …

Distributed edge computing has been advocated as a key enabling technology to tackle
large-scale intelligence applications, which is however hampered by the straggling effect. To …

We consider a mobile edge computing scenario where users want to perform a linear
inference operation $\boldsymbol {W}\boldsymbol {x} $ on local data $\boldsymbol {x} $ for …