Distributed control systems require high reliability and availability guarantees, despite often being deployed at the edge of network infrastructure. Edge computing resources are less secure and less reliable than centralized resources in data centers. Replication and consensus protocols improve robustness to network faults and crashed or corrupted nodes, but these volatile environments can cause non-faulty nodes to temporarily diverge, increasing the time needed for replicas to converge on a consensus value, and give Byzantine attackers too much influence over the convergence process.

This research introduces proximal Byzantine consensus, a new approximate consensus protocol where clients use statistical models of streaming computations to decide a consensus value. In addition, it provides an interval around the decision value and the probability that the true (non-faulty, noise-free) value falls within this interval. Proximal consensus (PC) tolerates unreliable network conditions, Byzantine behavior, and other sources of noise that cause honest replica states to diverge. We evaluate our approach for scalar values, and compare PC simulations against a vector consensus (VC) protocol simulation. Our simulations demonstrate that consensus values selected by PC have lower error and are more robust against Byzantine attacks. We formally characterize the security guarantees against Byzantine attacks and demonstrate attacker influence is bounded with high probability. We also provide an informal complexity analysis suggests PC scales better to higher dimensions than convex hull-based protocols such as VC.

Additionally, we present the future directions of this research thesis and transparently discuss known limitations, including ideas on how to overcome them.

Event Host: Roy Shadmon, Ph.D. Student, Computer Science and Engineering

Advisor: Owen Arden