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Davide Frey

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Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Applications Cryptography and Security cs.CY Machine Learning Social and Information Networks Software Engineering

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preprint2026arXiv

Context Adaptive Cooperation

As shown by Reliable Broadcast and Consensus, cooperation among a set of independent computing entities (sequential processes) is a central issue in distributed computing. Considering $n$-process asynchronous message-passing systems where some processes can be Byzantine, this paper introduces a new cooperation abstraction denoted Context-Adaptive Cooperation (CAC). While Reliable Broadcast is a one-to-$n$ cooperation abstraction and Consensus is an $n$-to-$n$ cooperation abstraction, CAC is a $d$-to-$n$ cooperation abstraction where the parameter $d$ ($1\leq d\leq n$) depends on the run and remains unknown to the processes. Moreover, the correct processes accept the same set of $\ell$ pairs $\langle v,i\rangle$ ($v$ is the value proposed by $p_i$) from the $d$ proposer processes, where $1 \leq \ell \leq d$ and, as $d$, $\ell$ remains unknown to the processes (except in specific cases). Those $\ell$ values are accepted one at a time in different orders at each process. Furthermore, CAC provides the processes with an imperfect oracle that gives information about the values that they may accept in the future. In a very interesting way, the CAC abstraction is particularly efficient in favorable circumstances. To illustrate its practical use, the paper describes in detail two applications that benefit from the abstraction: a fast consensus implementation under low contention (named Cascading Consensus), and a novel naming problem.

preprint2026arXiv

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning

Decentralized learning (DL) is an emerging machine learning paradigm where nodes collaboratively train models without a central server. However, the collaborative nature of DL makes it vulnerable to backdoor attacks, where a model is taught to behave normally on standard inputs while executing hidden, malicious actions when encountering data with specific triggers. Backdoor attacks in DL remain understudied and existing defenses often overlook DL constraints. We introduce Argus, a novel backdoor detection framework native to DL that requires neither a central coordinator nor prior knowledge of the trigger. In Argus, honest nodes locally analyze received model updates to identify potential backdoor triggers. Nodes then collectively share their triggers with their neighbors and use a structural similarity metric to separate true backdoors from false alarms induced by data heterogeneity. A key insight is that false positive triggers exhibit inconsistencies across participants while true positive ones show consistent patterns. Model updates that fail this collaborative test are rejected, and persistently malicious senders are eventually evicted. We provide the first theoretical convergence guarantees for a DL-specific backdoor detection mechanism, showing that filtering out suspicious model updates with high probability preserves a convergence rate comparable to standard DL. We implement and evaluate Argus on three standard datasets and against three state-of-the-art baselines. Across settings, Argus reduces attack success rates by up to 90 points compared to no defense, while preserving model utility within 5 percentage points of an omniscient oracle. Furthermore, the effectiveness of Argus compared to baselines improves as data heterogeneity increases.

preprint2022arXiv

Asynchronous Byzantine Reliable Broadcast With a Message Adversary

This paper considers the problem of reliable broadcast in asynchronous authenticated systems, in which n processes communicate using signed messages and up to t processes may behave arbitrarily (Byzantine processes). In addition, for each message m broadcast by a correct (i.e., non-Byzantine) process, a message adversary may prevent up to d correct processes from receiving m. (This message adversary captures network failures such as transient disconnections, silent churn, or message losses.) Considering such a "double" adversarial context and assuming n > 3t + 2d, a reliable broadcast algorithm is presented. Interestingly, when there is no message adversary (i.e., d = 0), the algorithm terminates in two communication steps (so, in this case, this algorithm is optimal in terms of both Byzantine tolerance and time efficiency). It is then shown that the condition n > 3t + 2d is necessary for implementing reliable broadcast in the presence of both Byzantine processes and a message adversary (whether the underlying system is enriched with signatures or not).

preprint2022arXiv

RAPTEE: Leveraging trusted execution environments for Byzantine-tolerant peer sampling services

Peer sampling is a first-class abstraction used in distributed systems for overlay management and information dissemination. The goal of peer sampling is to continuously build and refresh a partial and local view of the full membership of a dynamic, large-scale distributed system. Malicious nodes under the control of an adversary may aim at being over-represented in the views of correct nodes, increasing their impact on the proper operation of protocols built over peer sampling. State-of-the-art Byzantine resilient peer sampling protocols reduce this bias as long as Byzantines are not overly present. This paper studies the benefits brought to the resilience of peer sampling services when considering that a small portion of trusted nodes can run code whose authenticity and integrity can be assessed within a trusted execution environment, and specifically Intel's software guard extensions technology (SGX). We present RAPTEE, a protocol that builds and leverages trusted gossip-based communications to hamper an adversary's ability to increase its system-wide representation in the views of all nodes. We apply RAPTEE to BRAHMS, the most resilient peer sampling protocol to date. Experiments with 10,000 nodes show that with only 1% of SGX-capable devices, RAPTEE can reduce the proportion of Byzantine IDs in the view of honest nodes by up to 17% when the system contains 10% of Byzantine nodes. In addition, the security guarantees of RAPTEE hold even in the presence of a powerful attacker attempting to identify trusted nodes and injecting view-poisoned trusted nodes.

preprint2021arXiv

$\scriptstyle{BASALT}$: A Rock-Solid Foundation for Epidemic Consensus Algorithms in Very Large, Very Open Networks

Recent works have proposed new Byzantine consensus algorithms for blockchains based on epidemics, a design which enables highly scalable performance at a low cost. These methods however critically depend on a secure random peer sampling service: a service that provides a stream of random network nodes where no attacking entity can become over-represented. To ensure this security property, current epidemic platforms use a Proof-of-Stake system to select peer samples. However such a system limits the openness of the system as only nodes with significant stake can participate in the consensus, leading to an oligopoly situation. Moreover, this design introduces a complex interdependency between the consensus algorithm and the cryptocurrency built upon it. In this paper, we propose a radically different security design for the peer sampling service, based on the distribution of IP addresses to prevent Sybil attacks. We propose a new algorithm, $\scriptstyle{BASALT}$, that implements our design using a stubborn chaotic search to counter attackers' attempts at becoming over-represented. We show in theory and using Monte Carlo simulations that $\scriptstyle{BASALT}$ provides samples which are extremely close to the optimal distribution even in adversarial scenarios such as tentative Eclipse attacks. Live experiments on a production cryptocurrency platform confirm that the samples obtained using $\scriptstyle{BASALT}$ are equitably distributed amongst nodes, allowing for a system which is both open and where no single entity can gain excessive power.

preprint2021arXiv

Money Transfer Made Simple: a Specification, a Generic Algorithm, and its Proof

It has recently been shown that, contrarily to a common belief, money transfer in the presence of faulty (Byzantine) processes does not require strong agreement such as consensus. This article goes one step further: namely, it first proposes a non-sequential specification of the money-transfer object, and then presents a generic algorithm based on a simple FIFO order between each pair of processes that implements it. The genericity dimension lies in the underlying reliable broadcast abstraction which must be suited to the appropriate failure model. Interestingly, whatever the failure model, the money transfer algorithm only requires adding a single sequence number to its messages as control information. Moreover, as a side effect of the proposed algorithm, it follows that money transfer is a weaker problem than the construction of a safe/regular/atomic read/write register in the asynchronous message-passing crash-prone model.

preprint2020arXiv

CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics

The world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. National governments have problems evaluating the reach of the epidemic, due to having limited resources and tests at their disposal. This problem is especially acute in low and middle-income countries (LMICs). Hence, any simple, cheap and flexible means of evaluating the incidence and evolution of the epidemic in a given country with a reasonable level of accuracy is useful. In this paper, we propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts. This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts, and collects information from a larger fraction of the population (as compared to individual surveys). This technique has been deployed in the CoronaSurveys project, which has been collecting reports for the COVID-19 pandemic for more than two months. Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.

preprint2016arXiv

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 $Δ+ 1$ colors where $Δ$ 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 $O(d Δ)$, 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.