Federated learning (FL) is a promising machine learning paradigm that enables cross-party
data collaboration for real-world AI applications in a privacypreserving and law-regulated …

The performance of machine learning algorithms can be considerably improved when
trained over larger datasets. In many domains, such as medicine and finance, larger …

In this study we propose a new paradigm for solving DCOPs, whereby the agents delegate
the computational task to a set of external mediators who perform the computations for them …

In this study, we propose a new paradigm for solving DCOPs, whereby the agents delegate
the computational task to a set of external mediators who perform the computations for them …

The kidney exchange problem (KEP) seeks to determine a constellation of exchanges that
maximizes the number of possible transplants between a set of patients and their …

A secure multiparty computation (MPC) allows several parties to compute a function over
their inputs while keeping their inputs private. In its basic setting, the protocol involves only …

Students' decisions on whether to take a class are strongly affected by whether their friends
plan to take the class with them. A student may prefer to be assigned to a course they likes …

Course allocation, ie, the problem of assigning students to courses, is a difficult problem.
Students value being assigned to the same course as their friends. We propose a model that …

Several optimization scenarios involve multiple agents that desire to protect the privacy of
their preferences. There are distributed algorithms for constraint optimization that provide …

Abstract Distributed Constraint Optimization Problems (DCOPs) are a widely studied
framework for coordinating interactions in cooperative multi-agent systems. In classical …