70,209 research outputs found
Consensus in Equilibrium: Can One Against All Decide Fairly?
Is there an equilibrium for distributed consensus when all agents except one collude to steer the decision value towards their preference? If an equilibrium exists, then an n-1 size coalition cannot do better by deviating from the algorithm, even if it prefers a different decision value. We show that an equilibrium exists under this condition only if the number of agents in the network is odd and the decision is binary (among two possible input values). That is, in this framework we provide a separation between binary and multi-valued consensus. Moreover, the input and output distribution must be uniform, regardless of the communication model (synchronous or asynchronous). Furthermore, we define a new problem - Resilient Input Sharing (RIS), and use it to find an iff condition for the (n-1)-resilient equilibrium for deterministic binary consensus, essentially showing that an equilibrium for deterministic consensus is equivalent to each agent learning all the other inputs in some strong sense. Finally, we note that (n-2)-resilient equilibrium for binary consensus is possible for any n. The case of (n-2)-resilient equilibrium for multi-valued consensus is left open
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework
There has been a growing interest in realizing the resilient consensus of the
multi-agent system (MAS) under cyber-attacks, which aims to achieve the
consensus of normal agents (i.e., agents without attacks) in a network,
depending on the neighboring information. The literature has developed
mean-subsequence-reduced (MSR) algorithms for the MAS with F adversarial
attacks and has shown that the consensus is achieved for the normal agents when
the communication network is at least (2F+1)-robust. However, such a stringent
requirement on the communication network needs to be relaxed to enable more
practical applications. Our objective is, for the first time, to achieve less
stringent conditions on the network, while ensuring the resilient consensus for
the general linear MAS subject to control input constraints. In this work, we
propose a distributed resilient consensus framework, consisting of a
pre-designed consensus protocol and distributed model predictive control (DMPC)
optimization, which can help significantly reduce the requirement on the
network robustness and effectively handle the general linear constrained MAS
under adversarial attacks. By employing a novel distributed adversarial attack
detection mechanism based on the history information broadcast by neighbors and
a convex set (i.e., resilience set), we can evaluate the reliability of
communication links. Moreover, we show that the recursive feasibility of the
associated DMPC optimization problem can be guaranteed. The proposed consensus
protocol features the following properties: 1) by minimizing a group of control
variables, the consensus performance is optimized; 2) the resilient consensus
of the general linear constrained MAS subject to F-locally adversarial attacks
is achieved when the communication network is (F+1)-robust. Finally, numerical
simulation results are presented to verify the theoretical results
Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search
Modern machine learning (ML) models are capable of impressive performances.
However, their prowess is not due only to the improvements in their
architecture and training algorithms but also to a drastic increase in
computational power used to train them.
Such a drastic increase led to a growing interest in distributed ML, which in
turn made worker failures and adversarial attacks an increasingly pressing
concern. While distributed byzantine resilient algorithms have been proposed in
a differentiable setting, none exist in a gradient-free setting.
The goal of this work is to address this shortcoming. For that, we introduce
a more general definition of byzantine-resilience in ML - the
\textit{model-consensus}, that extends the definition of the classical
distributed consensus. We then leverage this definition to show that a general
class of gradient-free ML algorithms - ()-Evolutionary Search - can
be combined with classical distributed consensus algorithms to generate
gradient-free byzantine-resilient distributed learning algorithms. We provide
proofs and pseudo-code for two specific cases - the Total Order Broadcast and
proof-of-work leader election.Comment: 10 pages, 4 listings, 2 theorem
Resilient Cluster Consensus of Multiagent Systems
We investigate the problems of resilient cluster consensus in directed networks under three types of multiagent dynamics, namely, continuous-time multiagent systems, discrete-time multiagent systems, and switched multiagent systems composed of both continuous-time and discrete-time components. Resilient cluster censoring strategies are proposed to ensure cluster consensus against locally bounded Byzantine nodes in a purely distributed manner, where neither the number/identity of Byzantine nodes nor the division of clusters is assumed. We do not require complicated algebraic conditions or any balance conditions over intercluster structures, distinguishing the current work from previous results on cluster consensus problems besides a fortiori the attack-tolerant feature. Sufficient conditions are established in all the three scenarios based on the graph robustness. Furthermore, we solve the heterogenous cluster robustness problems and resilient scaled cluster consensus problems as extensions. The theoretical results are illustrated through numerical examples including the Santa Fe collaboration network
An Alloy Verification Model for Consensus-Based Auction Protocols
Max Consensus-based Auction (MCA) protocols are an elegant approach to
establish conflict-free distributed allocations in a wide range of network
utility maximization problems. A set of agents independently bid on a set of
items, and exchange their bids with their first hop-neighbors for a distributed
(max-consensus) winner determination. The use of MCA protocols was proposed,
, to solve the task allocation problem for a fleet of unmanned aerial
vehicles, in smart grids, or in distributed virtual network management
applications. Misconfigured or malicious agents participating in a MCA, or an
incorrect instantiation of policies can lead to oscillations of the protocol,
causing, , Service Level Agreement (SLA) violations.
In this paper, we propose a formal, machine-readable, Max-Consensus Auction
model, encoded in the Alloy lightweight modeling language. The model consists
of a network of agents applying the MCA mechanisms, instantiated with
potentially different policies, and a set of predicates to analyze its
convergence properties. We were able to verify that MCA is not resilient
against rebidding attacks, and that the protocol fails (to achieve a
conflict-free resource allocation) for some specific combinations of policies.
Our model can be used to verify, with a "push-button" analysis, the convergence
of the MCA mechanism to a conflict-free allocation of a wide range of policy
instantiations
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