Repositori URV

Identificador: imarina:7474008

Handle: https://hdl.handle.net/20.500.11797/imarina7474008

Autores:
Domingo-Ferrer, JosepBlanco-Justicia, AlbertoSanchez, DavidJebreel, Najeeb

Resumen:
© 2020 IEEE. Outsourcing computation allows wielding huge computational power. Even though cloud computing is the most usual type of outsourcing, resorting to idle edge devices for decentralized computation is an increasingly attractive alternative. We tackle the problem of making peer honesty and thus computation correctness self-enforcing in decentralized computing with untrusted peers. To do so, we leverage the co-utility property, which characterizes a situation in which honest co-operation is the best rational option to take even for purely selfish agents; in particular, if a protocol is co-utile, it is self-enforcing. Reputation is a powerful incentive that can make a P2P protocol co-utile. We present a co-utile P2P decentralized computing protocol that builds on a decentralized reputation calculation, which is itself co-utile and therefore self-enforcing. In this protocol, peers are given a computational task including code and data and they are incentivized to compute it correctly. Based also on co-utile reputation, we then present a protocol for federated learning, whereby peers compute on their local private data and have no incentive to randomly attack or poison the model. Our experiments show the viability of our co-utile approach to obtain correct results in both decentralized computation and federated learning.