We consider a version ofmultiprocessor scheduling with the special feature that jobs may be
rejected at a certain penalty. An instance of the problem is given by m identical parallel …

Originated from distributed learning, federated learning enables privacy-preserved
collaboration on a new abstracted level by sharing the model parameters only. While the …

This paper presents a survey of single machine scheduling problem with uniform parallel
machines. The single machine scheduling problem with uniform parallel machines consists …

We present a polynomial-time approximation scheme (PTAS) for the minimum dominating
set problem in unit disk graphs. In contrast to previously known approximation schemes for …

We investigate the problem of online scheduling of jobs to minimize flow time and stretch on
m identical machines. We consider the case where the algorithm is given either (1+ ε) m …

We consider the classical problem of scheduling parallel unrelated machines. Each job is to
be processed by exactly one machine. Processing job j on machine i requires time pij. The …

We study the problem of maximizing Nash welfare (MNW) while allocating indivisible goods
to asymmetric agents. The Nash welfare of an allocation is the weighted geometric mean of …

Federated learning learns a collaborative model by aggregating locally-computed updates
from mobile devices for privacy preservation. While current research typically prioritizing the …

We give a polynomial approximation scheme for the problem of scheduling on uniformly
related parallel machines for a large class of objective functions that depend only on the …

We study the problem of scheduling permanent jobs on unrelated machines when the
objective is to minimize the Lp norm of the machine loads. The problem is known as load …