Optimal Collision/Conflict-free Distance-2 Coloring in Synchronous Broadcast/Receive Tree Networks

This article is on message-passing systems where communication is (a) synchronous and (b) based on the "broadcast/receive" pair of communication operations. "Synchronous" means that time is discrete and appears as a sequence of time slots (or rounds) such that each message is received in the very same round in which it is sent. "Broadcast/receive" means that during a round a process can either broadcast a message to its neighbors or receive a message from one of them. In such a communication model, no two neighbors of the same process, nor a process and any of its neighbors, must be allowed to broadcast during the same time slot (thereby preventing message collisions in the first case, and message conflicts in the second case). From a graph theory point of view, the allocation of slots to processes is know as the distance-2 coloring problem: a color must be associated with each process (defining the time slots in which it will be allowed to broadcast) in such a way that any two processes at distance at most 2 obtain different colors, while the total number of colors is "as small as possible". The paper presents a parallel message-passing distance-2 coloring algorithm suited to trees, whose roots are dynamically defined. This algorithm, which is itself collision-free and conflict-free, uses $\Delta + 1$ colors where $\Delta$ is the maximal degree of the graph (hence the algorithm is color-optimal). It does not require all processes to have different initial identities, and its time complexity is $O(d \Delta)$, where d is the depth of the tree. As far as we know, this is the first distributed distance-2 coloring algorithm designed for the broadcast/receive round-based communication model, which owns all the previous properties.Comment: 19 pages including one appendix. One Figur

Good-Case Early-Stopping Latency of Synchronous Byzantine Reliable Broadcast: The Deterministic Case

This paper considers the good-case latency of Byzantine Reliable Broadcast (BRB), i.e., the time taken by correct processes to deliver a message when the initial sender is correct, and an essential property for practical distributed systems. Although significant strides have been made in recent years on this question, progress has mainly focused on either asynchronous or randomized algorithms. By contrast, the good-case latency of deterministic synchronous BRB under a majority of Byzantine faults has been little studied. In particular, it was not known whether a good-case latency below the worst-case bound of t+1 rounds could be obtained under a Byzantine majority. In this work, we answer this open question positively and propose a deterministic synchronous Byzantine reliable broadcast that achieves a good-case latency of max(2,t+3-c) rounds, where t is the upper bound on the number of Byzantine processes, and c the number of effectively correct processes

A Modular Approach to Construct Signature-Free BRB Algorithms Under a Message Adversary

This paper explores how reliable broadcast can be implemented without signatures when facing a dual adversary that can both corrupt processes and remove messages. More precisely, we consider an asynchronous n-process message-passing system in which up to t processes are Byzantine and where, at the network level, for each message broadcast by a correct process, an adversary can prevent up to d processes from receiving it (the integer d defines the power of the message adversary). So, unlike previous works, this work considers that not only can computing entities be faulty (Byzantine processes), but, in addition, that the network can also lose messages. To this end, the paper adopts a modular strategy and first introduces a new basic communication abstraction denoted k2?-cast, which simplifies quorum engineering, and studies its properties in this new adversarial context. Then, the paper deconstructs existing signature-free Byzantine-tolerant asynchronous broadcast algorithms and, with the help of the k2?-cast communication abstraction, reconstructs versions of them that tolerate both Byzantine processes and message adversaries. Interestingly, these reconstructed algorithms are also more efficient than the Byzantine-tolerant-only algorithms from which they originate

Context-Aware Publish Subscribe in Mobile ad Hoc Networks

The publish-subscribe communication paradigm is enjoying increasing popularity thanks to its ability to simplify the development of complex distributed applications. However, existing solutions in the publish-subscribe domain address only part of the challenges associated with the development of applications in dynamic scenarios such as mobile ad hoc networks. Mobile applications must be able to assist users in a variety of situations, responding not only to their inputs but also to the characteristics of the environment in which they operate. In this paper, we address these challenges by extending the publish-subscribe paradigm with the ability to manage and exploit context information when matching events against subscriptions. We present our extension in terms of a formal model of context-aware publish-subscribe. We propose a solution for its implementation in MANETs; and finally we validate our approach by means of extensive simulations

Exploring the Geography of Tags in Youtube Views

Although tags play a critical role in many social media,their link to the geographic distribution of user generatedvideos has been little investigated. In this paper, we ana-lyze the correlation between the geographic distribution ofa video’s views and the tags attached to this video in aYoutube dataset. We show that tags can be interpreted asmarkers of a video’s geographic diffusion, with some tagsstrongly linked to well identified geographic areas. Basedon our findings, we explore whether the distribution of avideo’s views can be predicted from its tags. We demon-strate how this predictive power could help improve on-linevideo services by preferentially storing videos close to wherethey are likely to be viewed. Our results show that even witha simplistic approach we are able to predict a minimum of65.9% of a video’s views for a majority of videos, and thata tag-based placement strategy can improve the hit rate ofa distributed on-line video service by up to 6.8% globally,with an improvement of up to 34% in the USA

The Synchronization Power (Consensus Number) of Access-Control Objects: The Case of AllowList and DenyList

This article studies the synchronization power of AllowList and DenyList objects under the lens provided by Herlihy's consensus hierarchy. It specifies AllowList and DenyList as distributed objects and shows that while they can both be seen as specializations of a more general object type, they inherently have different synchronization properties. While the AllowList object does not require synchronization between participating processes, a DenyList object requires processes to reach consensus on a specific set of processes. These results are then applied to the analysis of anonymity-preserving systems that use AllowList and DenyList objects. First, a blind-signature-based e-voting is presented. Then DenyList and AllowList objects are used to determine the consensus number of a specific decentralized key management system. Finally, an anonymous money transfer protocol using the association of AllowList and DenyList objects is studied.Comment: 27 pages, 10 figures, conferenc

Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks

Artificial intelligence relying on machine learning is increasingly used on small, personal, network-connected devices such as smartphones and vocal assistants, and these applications will likely evolve with the development of the Internet of Things. The learning process requires a lot of data, often real users’ data, and computing power. Decentralized machine learning can help to protect users’ privacy by keeping sensitive training data on users’ devices, and has the potential to alleviate the cost born by service providers by off-loading some of the learning effort to user devices. Unfortunately, most approaches proposed so far for distributed learning with neural network are mono-task, and do not transfer easily to multi-tasks problems, for which users seek to solve related but distinct learning tasks and the few existing multi-task approaches have serious limitations. In this paper, we propose a novel learning method for neural networks that is decentralized, multitask, and keeps users’ data local. Our approach works with different learning algorithms, on various types of neural networks. We formally analyze the convergence of our method, and we evaluateits efficiency in different situations on various kind of neural networks, with different learning algorithms, thus demonstrating its benefits in terms of learning quality and convergence

Good-case Early-Stopping Latency of Synchronous Byzantine Reliable Broadcast: The Deterministic Case (Extended Version)

This paper considers the good-case latency of Byzantine Reliable Broadcast (BRB), i.e., the time taken by correct processes to deliver a message when the initial sender is correct. This time plays a crucial role in the performance of practical distributed systems. Although significant strides have been made in recent years on this question, progress has mainly focused on either asynchronous or randomized algorithms. By contrast, the good-case latency of deterministic synchronous BRB under a majority of Byzantine faults has been little studied. In particular, it was not known whether a goodcase latency below the worst-case bound of t + 1 rounds could be obtained. This work answers this open question positively and proposes a deterministic synchronous Byzantine reliable broadcast that achieves a good-case latency of max(2, t + 3 -- c) rounds, where t is the upper bound on the number of Byzantine processes and c the number of effectively correct processes

Context Adaptive Cooperation

Reliable broadcast and consensus are the two pillars that support a lot of non-trivial fault-tolerant distributed middleware and fault-tolerant distributed systems. While they have close definitions, they strongly differ in the underlying assumptions needed to implement each of them. Reliable broadcast can be implemented in asynchronous systems in the presence of crash or Byzantine failures while Consensus cannot. This key difference stems from the fact that consensus involves synchronization between multiple processes that concurrently propose values, while reliable broadcast simply involves delivering a message from a predefined sender. This paper strikes a balance between these two agreement abstractions in the presence of Byzantine failures. It proposes CAC, a novel agreement abstraction that enables multiple processes to broadcast messages simultaneously, while guaranteeing that (despite potential conflicts, asynchrony, and Byzantine behaviors) the non-faulty processes will agree on messages deliveries. We show that this novel abstraction can enable more efficient algorithms for a variety of applications (such as money transfer where several people can share a same account). This is obtained by focusing the need for synchronization only on the processes that actually need to synchronize