Rachid Guerraoui

Related publications (553)

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, John Stephan

Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant attention, prior works often assume the data held by the machines to be homogeneous, which is seldom true in practical settings. Data heterogeneity makes Byzantine ML considerably more challenging, since a Byzantine machine can hardly be distinguished from a non-Byzantine outlier. A few solutions have been proposed to tackle this issue, but these provide suboptimal probabilistic guarantees and fare poorly in practice. This paper closes the theoretical gap, achieving optimality and inducing good empirical results. In fact, we show how to automatically adapt existing solutions for (homogeneous) Byzantine ML to the heterogeneous setting through a powerful mechanism, we call nearest neighbor mixing (NNM), which boosts any standard robust distributed gradient descent variant to yield optimal Byzantine resilience under heterogeneity. We obtain similar guarantees (in expectation) by plugging NNM in the distributed stochastic heavy ball method, a practical substitute to distributed gradient descent. We obtain empirical results that significantly outperform state-of-the-art Byzantine ML solutions.

PMLR2023

Leaderless consensus

Karolos Antoniadis, Antoine Philippe Matthieu Desjardins, Vincent Gramoli, Rachid Guerraoui, Gauthier Jérôme Timothée Voron, Mihail Igor Zablotchi

Classic synchronous consensus algorithms are leaderless in that processes exchange proposals, choose the maximum value and decide when they see the same choice across a couple of rounds. Indulgent consensus algorithms are typically leader-based. Although they tolerate unexpected delays and find practical applications in blockchains running over an open network like the Internet, their performance is highly dependent on the activity of a single participant. This paper asks whether, under eventual synchrony, it is possible to deterministically solve consensus without a leader. The fact that the weakest failure detector to solve consensus is one that also eventually elects a leader seems to indicate that the answer to the question is negative. We prove in this paper that the answer is actually positive.We first give a precise definition of the very notion of a leaderless algorithm. Then we present three indulgent leaderless consensus algorithms, each we believe interesting in its own right: (i) for shared memory, (ii) for message passing with omission failures and (iii) for message passing with Byzantine failures.Finally, we implement a Byzantine fault tolerant (BFT) state machine replication (SMR), that is leaderless. Our empirical results demonstrate that it is faster and more robust than HotStuff, the recent BFT SMR algorithm used in the Facebook Libra blockchain when deployed in a wide area network.(c) 2023 Elsevier Inc. All rights reserved.

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, John Stephan

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied independently in distributed ML, their synthesis remains poorly understood. We present the first tight analysis of the error incurred by any algorithm ensuring robustness against a fraction of adversarial machines, as well as differential privacy (DP) for honest machines' data against any other curious entity. Our analysis exhibits a fundamental trade-off between privacy, robustness, and utility. To prove our lower bound, we consider the case of mean estimation, subject to distributed DP and robustness constraints, and devise reductions to centralized estimation of one-way marginals. We prove our matching upper bound by presenting a new distributed ML algorithm using a high-dimensional robust aggregation rule. The latter amortizes the dependence on the dimension in the error (caused by adversarial workers and DP), while being agnostic to the statistical properties of the data.

2023

On the Strategyproofness of the Geometric Median

El Mahdi El Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Le Nguyen Hoang

The geometric median, an instrumental component of the secure machine learning toolbox, is known to be effective when robustly aggregating models (or gradients), gathered from potentially malicious (or strategic) users. What is less known is the extent to which the geometric median incentivizes dishonest behaviors. This paper addresses this fundamental question by quantifying its strategyproofness. While we observe that the geometric median is not even approximately strategyproof, we prove that it is asymptotically α-strategyproof: when the number of users is large enough, a user that misbehaves can gain at most a multiplicative factor α, which we compute as a function of the distribution followed by the users. We then generalize our results to the case where users actually care more about specific dimensions, determining how this impacts α. We also show how the skewed geometric medians can be used to improve strategyproofness.

2023

uBFT: Microsecond-Scale BFT using Disaggregated Memory

Naama Ben David, Rachid Guerraoui, Antoine Murat, Athanasios Xygkis, Mihail Igor Zablotchi

We propose uBFT, the first State Machine Replication (SMR) system to achieve microsecond-scale latency in data centers, while using only 2f+1 replicas to tolerate f Byzantine failures. The Byzantine Fault Tolerance (BFT) provided by uBFT is essential as pure crashes appear to be a mere illusion with real-life systems reportedly failing in many unexpected ways. uBFT relies on a small non-tailored trusted computing base—disaggregated memory—and consumes a practically bounded amount of memory. uBFT is based on a novel abstraction called Consistent Tail Broadcast, which we use to prevent equivocation while bounding memory. We implement uBFT using RDMA-based disaggregated memory and obtain an end-to-end latency of as little as 10 us. This is at least 50× faster than MinBFT, a state-of-the-art 2f+1 BFT SMR based on Intel’s SGX. We use uBFT to replicate two KV-stores (Memcached and Redis), as well as a financial order matching engine (Liquibook). These applications have low latency (up to 20 us) and become Byzantine tolerant with as little as 10 us more. The price for uBFT is a small amount of reliable disaggregated memory (less than 1 MiB), which in our prototype consists of a small number of memory servers connected through RDMA and replicated for fault tolerance.

2023

uKharon: A Membership Service for Microsecond Applications

Rachid Guerraoui, Antoine Murat, Javier Picorel Obando, Athanasios Xygkis

Modern data center fabrics open the possibility of microsecond distributed applications, such as data stores and message queues. A challenging aspect of their development is to ensure that, besides being fast in the common case, these applications react fast to changes in their membership, e.g., due to reconfiguration and failures. This is especially important as they form the backbone of numerous cloud-powered services, such as analytics and trading systems, trying to meet ever-stringent tail latency requirements. As the microservices-oriented architecture is the de facto standard for building cloud services, a single user request translates to a wide fan-out of microservices interactions sitting on the critical path. The outcome is implacable: the traditionally uncommon events of reconfiguration and failures are exacerbated by the fan-out of communication, making user requests commonly experience such events and quickly impacting the tail latency of the service. We present uKharon, a microsecond-scale membership service that detects changes in the membership of applications and lets them failover in as little as 50us. uKharon consists of (1) a multi-level failure detector, (2) a consensus engine that relies on one-sided RDMA CAS, and (3) minimal-overhead membership leases, all exploiting RDMA to operate at the microsecond scale. We showcase the power of uKharon by building uKharon-KV, a replicated Key-Value cache based on HERD. uKharon-KV processes PUT requests as fast as the state-of-the-art and improves upon it by (1) removing the need for replicating GET requests and (2) bringing the end-to-end failover down to 53us, a 10x improvement.

USENIX Association2023

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Sadegh Farhadkhani, Rachid Guerraoui, Le Nguyen Hoang

To study the resilience of distributed learning, the “Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes been considered unrealistic, when the workers are mostly trustworthy machines. In this paper, we show a surprising equivalence between this model and data poisoning, a threat considered much more realistic. More specifically, we prove that every gradient attack can be reduced to data poisoning, in any personalized federated learning system with PAC guarantees (which we show are both desirable and realistic). This equivalence makes it possible to obtain new impossibility results on the resilience of any “robust” learning algorithm to data poisoning in highly heterogeneous applications, as corollaries of existing impossibility theorems on Byzantine machine learning. Moreover, using our equivalence, we derive a practical attack that we show (theoretically and empirically) can be very effective against classical personalized federated learning models.

PMLR2022

As easy as ABC: Optimal (A)ccountable (B)yzantine (C)onsensus is easy!

Seth Gilbert, Vincent Gramoli, Rachid Guerraoui, Jovan Komatovic

It is known that the agreement property of the Byzantine consensus problem among n processes can be violated in a non-synchronous system if the number of faulty processes exceeds t0 = ┌n/3┐ − 1 [10], [19]. In this paper, we investigate the accountable Byzantine consensus problem in non-synchronous systems: the problem of solving Byzantine consensus whenever possible (e.g., when the number of faulty processes does not exceed t0) and allowing correct processes to obtain proof of culpability of (at least) t0+1 faulty processes whenever correct processes disagree. We present four complementary contributions: 1) We introduce ABC: a simple yet efficient transformation of any Byzantine consensus protocol to an accountable one. ABC introduces an overhead of only two all-to-all communication rounds and O(n2) additional bits in executions with up to t0 faults (i.e., in the common case). 2) We define the accountability complexity, a complex-ity metric representing the number of accountability-specific messages that correct processes must send. Fur-thermore, we prove a tight lower bound. In particular, we show that any accountable Byzantine consensus protocol incurs cubic accountability complexity. Moreover, we illustrate that the bound is tight by applying the ABC transformation to any Byzantine consensus protocol. 3) We demonstrate that, when applied to an optimal Byzan-tine consensus protocol, ABC constructs an accountable Byzantine consensus protocol that is (1) optimal with respect to the communication complexity in solving consensus whenever consensus is solvable, and (2) op-timal with respect to the accountability complexity in obtaining accountability whenever disagreement occurs. 4) We generalize ABC to other distributed computing prob-lems besides the classic consensus problem. We charac-terize a class of agreement tasks, including reliable and consistent broadcast [5], that ABC renders accountable.

IEEE2022

Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony!

Seth Gilbert, Vincent Gramoli, Rachid Guerraoui, Jovan Komatovic, Manuel José Ribeiro Vidigueira

The Dolev-Reischuk bound says that any deterministic Byzantine consensus protocol has (at least) quadratic communication complexity in the worst case. While it has been shown that the bound is tight in synchronous environments, it is still unknown whether a consensus protocol with quadratic communication complexity can be obtained in partial synchrony. Until now, the most efficient known solutions for Byzantine consensus in partially synchronous settings had cubic communication complexity (e.g., HotStuff, binary DBFT). This paper closes the existing gap by introducing SQuad, a partially synchronous Byzantine consensus protocol with quadratic worst-case communication complexity. In addition, SQuad is optimally-resilient and achieves linear worst-case latency complexity. The key technical contribution underlying SQuad lies in the way we solve view synchronization, the problem of bringing all correct processes to the same view with a correct leader for sufficiently long. Concretely, we present RareSync, a view synchronization protocol with quadratic communication complexity and linear latency complexity, which we utilize in order to obtain SQuad.

Dagstuhl Publishing2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, John Stephan

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence of some misbehaving (a.k.a., Byzantine) workers. Although a myriad of techniques addressing the problem have been proposed, the field arguably rests on fragile foundations. These techniques are hard to prove correct and rely on assumptions that are (a) quite unrealistic, i.e., often violated in practice, and (b) heterogeneous, i.e., making it difficult to compare approaches. We present RESAM (RESilient Averaging of Momentums), a unified framework that makes it simple to establish optimal Byzantine resilience, relying only on standard machine learning assumptions. Our framework is mainly composed of two operators: resilient averaging at the server and distributed momentum at the workers. We prove a general theorem stating the convergence of distributed SGD under RESAM. Interestingly, demonstrating and comparing the convergence of many existing techniques become direct corollaries of our theorem, without resorting to stringent assumptions. We also present an empirical evaluation of the practical relevance of RESAM.

PMLR2022