Dealing with Unreliable Agents in Dynamic Gossip

International audienceGossip describes the spread of information throughout a network of agents. It investigates how agents, each starting with a unique secret, can efficiently make peer-to-peer calls so that ultimately everyone knows all secrets. In Dynamic Gossip, agents share phone numbers in addition to secrets, which allows the network to grow at run-time. Most gossip protocols assume that all agents are reliable, but this is not given for many practical applications. We drop this assumption and study Dynamic Gossip with unreliable agents. The aim is then for agents to learn all secrets of the reliable agents and to identify the unreliable agents. We show that with unreliable agents classic results on Dynamic Gossip no longer hold. Specifically, the Learn New Secrets protocol is no longer characterised by the same class of graphs, so-called sun graphs. In addition, we show that unreliable agents that do not initiate communication are harder to identify than agents that do. This has paradoxical consequences for measures against unreliability, for example to combat the spread of fake news in social networks

A failure detector for HPC platforms

International audienc

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Asynchronous Implementation of Failure Detectors with partial connectivity and unknown participants

The distributed computing scenario is rapidly evolving for integrating selforganizing and dynamic wireless networks. Unreliable failure detectors are classical mechanisms which provide information about process failures and can help systems to cope with the high dynamism of these networks. A number of failure detection algorithms has been proposed so far. Nonetheless, most of them assume a global knowledge about the membership as well as a fully communication connectivity; additionally, they are timer-based, requiring that eventually some bound on the message transmission will permanently hold. These assumptions are no longer appropriate to the new scenario. This paper presents a new failure detector protocol which implements a new class of detectors, namely S(M), which adapts the properties of the S class to a dynamic network with an unknown membership. It has the interesting feature to be time-free, so that it does not rely on timers to detect failures; moreover, it tolerates mobility of nodes and message losses.L'informatique répartie intègre de plus en plus des réseaux sans fil dynamiques et auto-organisant. Les détecteurs de fautes non fiables sont un mécanisme classique fournissant des informations sur les processus défaillants. Ils peuvent être particulièrement utiles pour gérer le dynamisme important de ces réseaux. De nombreux algorithmes de détection de fautes ont déjà été proposés. Cependant, la plupart d'entre eux considèrent un ensemble connu de processus interconnectés par un réseau complètement maillé. De plus, ces détecteurs reposent sur des temporisateurs et supposent à terme des bornes sur les délais de transmission des messages. Des telles hypothèses ne sont pas réalistes dans les environnements dynamiques. Cet article présente un nouveau protocole pour détecter les fautes qui implémente une nouvelle classe de détecteurs, appelé S(M), qui adapte les propriétés de la classe S aux réseaux dynamiques avec l'absence de la connaissance des participants. Notre détecteur ne repose sur aucun temporisateur ; de plus, il tolère la mobilité des noeuds et la perte de messages

A Context-aware Trust Framework for Resilient Distributed Cooperative Spectrum Sensing in Dynamic Settings

Cognitive radios enable dynamic spectrum access where secondary users (SUs) are allowed to operate on the licensed spectrum bands on an opportunistic noninterference basis. Cooperation among the SUs for spectrum sensing is essential for environments with deep shadows. In this paper, we study the adverse effect of insistent spectrum sensing data falsification (ISSDF) attack on iterative distributed cooperative spectrum sensing. We show that the existing trust management schemes are not adequate in mitigating ISSDF attacks in dynamic settings where the primary user (PU) of the band frequently transitions between active and inactive states. We propose a novel context-aware distributed trust framework for cooperative spectrum sensing in mobile cognitive radio ad hoc networks (CRAHN) that effectively alleviates different types of ISSDF attacks (Always-Yes, Always-No, and fabricating) in dynamic scenarios. In the proposed framework, the SU nodes evaluate the trustworthiness of one another based on the two possible contexts in which they make observations from each other: PU absent context and PU present context. We evaluate the proposed context-aware scheme and compare it against the existing context-oblivious trust schemes using theoretical analysis and extensive simulations of realistic scenarios of mobile CRAHNs operating in TV white space. We show that in the presence of a large set of attackers (as high as 60% of the network), the proposed context-aware trust scheme successfully mitigates the attacks and satisfy the false alarm and missed-detection rates of $10^{-2}$ and lower. Moreover, we show that the proposed scheme is scalable in terms of attack severity, SU network density, and the distance of the SU network to the PU transmitter

Deep Learning at Scale with Nearest Neighbours Communications

As deep learning techniques become more and more popular, there is the need to move these applications from the data scientist’s Jupyter notebook to efficient and reliable enterprise solutions. Moreover, distributed training of deep learning models will happen more and more outside the well-known borders of cloud and HPC infrastructure and will move to edge and mobile platforms. Current techniques for distributed deep learning have drawbacks in both these scenarios, limiting their long-term applicability. After a critical review of the established techniques for Data Parallel training from both a distributed computing and deep learning perspective, a novel approach based on nearest-neighbour communications is presented in order to overcome some of the issues related to mainstream approaches, such as global communication patterns. Moreover, in order to validate the proposed strategy, the Flexible Asynchronous Scalable Training (FAST) framework is introduced, which allows to apply the nearest-neighbours communications approach to a deep learning framework of choice. Finally, a relevant use-case is deployed on a medium-scale infrastructure to demonstrate both the framework and the methodology presented. Training convergence and scalability results are presented and discussed in comparison to a baseline defined by using state-of-the-art distributed training tools provided by a well-known deep learning framework