Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. While man-made systems often employ intricate structural solutions to overcome noise, the structure of many biological systems is more amorphous. It is not well understood how communication noise may affect the computational repertoire of such groups. To approach this question we consider the basic collective task of rumor spreading, in which information from few knowledgeable sources must reliably flow into the rest of the population. In order to study the effect of communication noise on the ability of groups that lack stable structures to efficiently solve this task, we consider a noisy version of the uniform PULL model. We prove a lower bound which implies that, in the presence of even moderate levels of noise that affect all facets of the communication, no scheme can significantly outperform the trivial one in which agents have to wait until directly interacting with the sources. Our results thus show an exponential separation between the uniform PUSH and PULL communication models in the presence of noise. Such separation may be interpreted as suggesting that, in order to achieve efficient rumor spreading, a system must exhibit either some degree of structural stability or, alternatively, some facet of the communication which is immune to noise. We corroborate our theoretical findings with a new analysis of experimental data regarding recruitment in Cataglyphis niger desert ants.
In Rumor Spreading problems (also referred to as Broadcast ) a piece of information typically held by a single designated agent is to be disseminated to the rest of the population. It is the subject of a vast literature in theoretical computer science, and more specifically in the distributed computing community, see, e.g., @cite_29 @cite_32 @cite_25 @cite_15 @cite_34 @cite_0 @cite_39 @cite_1 . While some works assume a fixed topology, the canonical setting does not assume a network. Instead agents communicate through uniform @math based interactions (including the phone call model), in which agents interact in pairs with other agents independently chosen at each time step uniformly at random from all agents in the population. The success of such protocols is largely due to their inherent simplicity and fault-tolerant resilience @cite_33 @cite_3 . In particular, it has been shown that under the @math model, there exist efficient rumor spreading protocol that uses a single bit per message and can overcome flips in messages (noise) @cite_35 .
@cite_35 @cite_33 @cite_29 @cite_1 @cite_32 @cite_3 @cite_39 @cite_0 @cite_15 @cite_34 @cite_25
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We investigate the randomness requirements of the classical rumor spreading problem on fully connected graphs with n vertices. In the standard random protocol, where each node that knows the rumor sends it to a randomly chosen neighbor in every round, each node needs O((log n)2) random bits in order to spread the rumor in O(log n) rounds with high probability (w.h.p.). For the simple quasirandom rumor spreading protocol proposed by Doerr, Friedrich, and Sauerwald (2008), [log n] random bits per node are sufficient. A lower bound by Doerr and Fouz (2009) shows that this is asymptotically tight for a slightly more general class of protocols, the so-called gate-model. In this paper, we consider general rumor spreading protocols. We provide a simple push-protocol that requires only a total of O(n log log n) random bits (i.e., on average O(log log n) bits per node) in order to spread the rumor in O(log n) rounds w.h.p. We also investigate the theoretical minimal randomness requirements of efficient rumor spreading. We prove the existence of a (non-uniform) push-protocol for which a total of 2 log n + log log n + o(log log n) random bits suffice to spread the rumor in log n + ln n + O(1) rounds with probability 1 − o(1). This is contrasted by a simple time-randomness tradeoff for the class of all rumor spreading protocols, according to which any protocol that uses log n − log log n − ω(1) random bits requires ω(log n) rounds to spread the rumor. Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems, wherein messages are often distorted before reaching their destination, is quite limited. In this study we aim at bridging this gap by rigorously analyzing a model of communication in large anonymous populations composed of simple agents which interact through short and highly unreliable messages. We focus on the rumor-spreading problem and the majority-consensus problem, two fundamental tasks in distributed computing, and initiate their study under communication noise. Our model for communication is extremely weak and follows the push gossip communication paradigm: In each synchronous round each agent that wishes to send information delivers a message to a random anonymous agent. This communication is further restricted to contain only one bit (essentially representing an opinion). Lastly, the system is assumed to be so noisy that the bit in each message sent is flipped independently with probability 1 2-e, for some small Ae >0. Even in this severely restricted, stochastic and noisy setting we give natural protocols that solve the noisy rumor-spreading and the noisy majority-consensus problems efficiently. Our protocols run in O(log n e2) rounds and use O(n log n e2) messages bits in total, where n is the number of agents. These bounds are asymptotically optimal and, in fact, are as fast and message efficient as if each agent would have been simultaneously informed directly by the source. Our efficient, robust, and simple algorithms suggest balancing between silence and transmission, synchronization, and majority-based decisions as important ingredients towards understanding collective communication schemes in anonymous and noisy populations. We study gossip algorithms for the rumor spreading problem which asks each node to deliver a rumor to all nodes in an unknown network. Gossip algorithms allow nodes only to call one neighbor per round and have recently attracted attention as message efficient, simple and robust solutions to the rumor spreading problem. Recently, non-uniform random gossip schemes were devised to allow efficient rumor spreading in networks with bottlenecks. In particular, [Censor-, STOC'12] gave an O(log^3 n) algorithm to solve the 1-local broadcast problem in which each node wants to exchange rumors locally with its 1-neighborhood. By repeatedly applying this protocol one can solve the global rumor spreading quickly for all networks with small diameter, independently of the conductance. This and all prior gossip algorithms for the rumor spreading problem have been inherently randomized in their design and analysis. This resulted in a parallel research direction trying to reduce and determine the amount of randomness needed for efficient rumor spreading. This has been done via lower bounds for restricted models and by designing gossip algorithms with a reduced need for randomness. The general intuition and consensus of these results has been that randomization plays a important role in effectively spreading rumors. In this paper we improves over this state of the art in several ways by presenting a deterministic gossip algorithm that solves the the k-local broadcast problem in 2(k+log n)log n rounds. Besides being the first efficient deterministic solution to the rumor spreading problem this algorithm is interesting in many aspects: It is simpler, more natural, more robust and faster than its randomized pendant and guarantees success with certainty instead of with high probability. Its analysis is furthermore simple, self-contained and fundamentally different from prior works. We present new distributed deterministic solutions to two communication problems in n-node ad-hoc radio networks: rumor gathering and multi-broadcast. In these problems, some or all nodes of the network initially contain input data called rumors, which have to be learned by other nodes. In rumor gathering, there are k rumors initially distributed arbitrarily among the nodes, and the goal is to collect all the rumors at one node. Our rumor gathering algorithm works in O((k + n) log n) time and our multi-broadcast algorithm works in O(k log3 n + n log4 n) time, for any n-node networks and k rumors (with arbitrary k), which is a substantial improvement over the best previously known deterministic solutions to these problems. As a consequence, we exponentially decrease the gap between upper and lower bounds on the deterministic time complexity of four communication problems: rumor gathering, multi-broadcast, gossiping and routing, in the important case when every node has initially at most one rumor (this is the scenario for gossiping and for the usual formulation of routing). Indeed, for k = O(n), our results simultaneously decrease the complexity gaps for these four problems from polynomial to polylogarithmic in the size of the graph. Moreover, our deterministic gathering algorithm applied for k = O(n) rumors, improves over the best previously known randomized algorithm of time O(k log n + n log2 n).