arXiv Open Access 2016

Defending Non-Bayesian Learning against Adversarial Attacks

Lili Su Nitin H. Vaidya

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Abstrak

This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of the world, and try to collaboratively learn the true state. We focus on the impact of the adversarial agents on the performance of consensus-based non-Bayesian learning, where non-faulty agents combine local learning updates with consensus primitives. In particular, we consider the scenario where an unknown subset of agents suffer Byzantine faults -- agents suffering Byzantine faults behave arbitrarily. Two different learning rules are proposed.

Topik & Kata Kunci

cs.DC cs.LG

Penulis (2)

Lili Su

Nitin H. Vaidya

Format Sitasi

APA MLA BibTeX

Su, L., Vaidya, N.H. (2016). Defending Non-Bayesian Learning against Adversarial Attacks. https://arxiv.org/abs/1606.08883

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓