3 Search Results for "El-Mhamdi, El-Mahdi"

Document
DOI: 10.4230/LIPIcs.DISC.2025.30
Team Formation and Applications

Authors: Yuval Emek, Shay Kutten, Ido Rafael, and Gadi Taubenfeld

Published in: LIPIcs, Volume 356, 39th International Symposium on Distributed Computing (DISC 2025)

Abstract
A novel long-lived distributed problem, called Team Formation (TF), is introduced together with a message- and time-efficient randomized algorithm. The problem is defined over the asynchronous model with a complete communication graph, using bounded size messages, where a certain fraction of the nodes may experience a generalized, strictly stronger, version of initial failures. The goal of a TF algorithm is to assemble tokens injected by the environment, in a distributed manner, into teams of size σ, where σ is a parameter of the problem. The usefulness of TF is demonstrated by using it to derive efficient algorithms for many distributed problems. Specifically, we show that various (one-shot as well as long-lived) distributed problems reduce to TF. This includes well-known (and extensively studied) distributed problems such as several versions of leader election and threshold detection. For example, we are the first to break the linear message complexity bound for asynchronous implicit leader election. We also improve the time complexity of message-optimal algorithms for asynchronous explicit leader election. Other distributed problems that reduce to TF are new ones, including matching players in online gaming platforms, a generalization of gathering, constructing a perfect matching in an induced subgraph of the complete graph, and more. To complement our positive contribution, we establish a tight lower bound on the message complexity of TF algorithms.
Cite as

Yuval Emek, Shay Kutten, Ido Rafael, and Gadi Taubenfeld. Team Formation and Applications. In 39th International Symposium on Distributed Computing (DISC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 356, pp. 30:1-30:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{emek_et_al:LIPIcs.DISC.2025.30,
  author =	{Emek, Yuval and Kutten, Shay and Rafael, Ido and Taubenfeld, Gadi},
  title =	{{Team Formation and Applications}},
  booktitle =	{39th International Symposium on Distributed Computing (DISC 2025)},
  pages =	{30:1--30:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-402-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{356},
  editor =	{Kowalski, Dariusz R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2025.30},
  URN =		{urn:nbn:de:0030-drops-248474},
  doi =		{10.4230/LIPIcs.DISC.2025.30},
  annote =	{Keywords: asynchronous message-passing, complete communication graph, initial failures, leader election, matching}
}
Document
Brief Announcement
DOI: 10.4230/LIPIcs.DISC.2025.64
Brief Announcement: Proximal Byzantine Agreement: Improved Accuracy for Fault-Tolerant Replicated Datastreams

Authors: Roy Shadmon and Owen Arden

Published in: LIPIcs, Volume 356, 39th International Symposium on Distributed Computing (DISC 2025)

Abstract
Approximate Byzantine Agreement (ABA) protocols enable nonfaulty replicas with different initial values to derive a values within a ε-neighborhood of each other, despite the presence of Byzantine behavior. While they give strong guarantees for this ε-agreement property, they tend to have weaker guarantees that the derived value is accurate with respect to some ground truth. Worse, they often have impractical requirements such as large replica sets proportional to data dimensionality, or a priori knowledge of the maximum distance between nonfaulty values. In Stochastic Byzantine Agreement (SBA), the distribution of the nonfaulty values is the result of a stochastic process influenced by sensor measurement error or other sources of noise that affect system outputs. For these scenarios, we present Proximal Byzantine Agreement (PBA), a stochastic Byzantine agreement protocol which infers the most likely output of replicated computation based on the proposed values observed by each replica. Unlike ABA protocols, PBA prioritizes accuracy over agreement. PBA accuracy is relative to the variance of nonfaulty values, yielding comparatively more accurate results for noisy data, particularly when noise is asymmetric. Our evaluations demonstrate this accuracy scales with data dimensionality, outperforming or only mildly underperforming methods that require quorums with up to 10× more replicas and 4× to 124× more computation time per agreement decision, even at relatively low dimensions (d = 4 to d = 18).
Cite as

Roy Shadmon and Owen Arden. Brief Announcement: Proximal Byzantine Agreement: Improved Accuracy for Fault-Tolerant Replicated Datastreams. In 39th International Symposium on Distributed Computing (DISC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 356, pp. 64:1-64:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shadmon_et_al:LIPIcs.DISC.2025.64,
  author =	{Shadmon, Roy and Arden, Owen},
  title =	{{Brief Announcement: Proximal Byzantine Agreement: Improved Accuracy for Fault-Tolerant Replicated Datastreams}},
  booktitle =	{39th International Symposium on Distributed Computing (DISC 2025)},
  pages =	{64:1--64:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-402-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{356},
  editor =	{Kowalski, Dariusz R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2025.64},
  URN =		{urn:nbn:de:0030-drops-248808},
  doi =		{10.4230/LIPIcs.DISC.2025.64},
  annote =	{Keywords: Byzantine fault tolerance, distributed control systems, robust statistics}
}
Document
DOI: 10.4230/LIPIcs.OPODIS.2020.8
AKSEL: Fast Byzantine SGD

Authors: Amine Boussetta, El-Mahdi El-Mhamdi, Rachid Guerraoui, Alexandre Maurer, and Sébastien Rouault

Published in: LIPIcs, Volume 184, 24th International Conference on Principles of Distributed Systems (OPODIS 2020)

Abstract
Modern machine learning architectures distinguish servers and workers. Typically, a d-dimensional model is hosted by a server and trained by n workers, using a distributed stochastic gradient descent (SGD) optimization scheme. At each SGD step, the goal is to estimate the gradient of a cost function. The simplest way to do this is to average the gradients estimated by the workers. However, averaging is not resilient to even one single Byzantine failure of a worker. Many alternative gradient aggregation rules (GARs) have recently been proposed to tolerate a maximum number f of Byzantine workers. These GARs differ according to (1) the complexity of their computation time, (2) the maximal number of Byzantine workers despite which convergence can still be ensured (breakdown point), and (3) their accuracy, which can be captured by (3.1) their angular error, namely the angle with the true gradient, as well as (3.2) their ability to aggregate full gradients. In particular, many are not full gradients for they operate on each dimension separately, which results in a coordinate-wise blended gradient, leading to low accuracy in practical situations where the number (s) of workers that are actually Byzantine in an execution is small (s < < f). We propose Aksel, a new scalable median-based GAR with optimal time complexity (𝒪(nd)), optimal breakdown point (n > 2f) and the lowest upper bound on the expected angular error (𝒪(√d)) among full gradient approaches. We also study the actual angular error of Aksel when the gradient distribution is normal and show that it only grows in 𝒪(√dlog{n}), which is the first logarithmic upper bound ever proven on the number of workers n assuming an optimal breakdown point. We also report on an empirical evaluation of Aksel on various classification tasks, which we compare to alternative GARs against state-of-the-art attacks. Aksel is the only GAR reaching top accuracy when there is actually none or few Byzantine workers while maintaining a good defense even under the extreme case (s = f). For simplicity of presentation, we consider a scheme with a single server. However, as we explain in the paper, Aksel can also easily be adapted to multi-server architectures that tolerate the Byzantine behavior of a fraction of the servers.
Cite as

Amine Boussetta, El-Mahdi El-Mhamdi, Rachid Guerraoui, Alexandre Maurer, and Sébastien Rouault. AKSEL: Fast Byzantine SGD. In 24th International Conference on Principles of Distributed Systems (OPODIS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 184, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{boussetta_et_al:LIPIcs.OPODIS.2020.8,
  author =	{Boussetta, Amine and El-Mhamdi, El-Mahdi and Guerraoui, Rachid and Maurer, Alexandre and Rouault, S\'{e}bastien},
  title =	{{AKSEL: Fast Byzantine SGD}},
  booktitle =	{24th International Conference on Principles of Distributed Systems (OPODIS 2020)},
  pages =	{8:1--8:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-176-4},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{184},
  editor =	{Bramas, Quentin and Oshman, Rotem and Romano, Paolo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2020.8},
  URN =		{urn:nbn:de:0030-drops-134931},
  doi =		{10.4230/LIPIcs.OPODIS.2020.8},
  annote =	{Keywords: Machine learning, Stochastic gradient descent, Byzantine failures}
}
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