Optimal gossip algorithm for distributed consensus SVM training in wireless sensor networks
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
ACC'09 Proceedings of the 2009 conference on American Control Conference
Greedy gossip with eavesdropping
IEEE Transactions on Signal Processing
Consensus of multi-agent networks in the presence of adversaries using only local information
Proceedings of the 1st international conference on High Confidence Networked Systems
Automatica (Journal of IFAC)
Algorithms for determining network robustness
Proceedings of the 2nd ACM international conference on High confidence networked systems
Graph diameter, eigenvalues, and minimum-time consensus
Automatica (Journal of IFAC)
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Given an arbitrary network of interconnected nodes, we develop and analyze a distributed strategy that enables a subset of the nodes to calculate any given function of the node values. Our scheme utilizes a linear iteration where, at each time-step, each node updates its value to be a weighted average of its own previous value and those of its neighbors. We show that this approach can be viewed as a linear dynamical system, with dynamics that are given by the weight matrix of the linear iteration, and with outputs for each node that are captured by the set of values that are available to that node at each time-step. In connected networks with time-invariant topologies, we use observability theory to show that after running the linear iteration for a finite number of time-steps with almost any choice of weight matrix, each node obtains enough information to calculate any arbitrary function of the initial node values. The problem of distributed consensus via linear iterations, where all nodes in the network calculate the same function, is treated as a special case of our approach. In particular, our scheme allows nodes in connected networks with time-invariant topologies to reach consensus on any arbitrary function of the initial node values in a finite number of steps for almost any choice of weight matrix.