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Exogenous MDPs (Exo-MDPs) capture sequential decision-making where uncertainty comes solely from exogenous inputs that evolve independently of the learner's actions. This structure is especially common in operations research applications…

Machine Learning · Computer Science 2026-01-29 Hao Liang , Jiayu Cheng , Sean R. Sinclair , Yali Du

A Connected Dominating Set (CDS) based virtual backbone plays an important role in wireless ad hoc networks for efficient routing and broadcasting. Each node in the network can select some of its 1-hop neighbors as Multi Point Relay (MPR)…

Networking and Internet Architecture · Computer Science 2012-04-25 R. Ramalakshmi , S. Radhakrishnan

The way to arrive at formula of zeta matrix for any graded posets with the finite set of minimal elements is delivered following the first reference. This is being achieved via adjacency and zeta matrix description of bipartite digraphs…

Combinatorics · Mathematics 2011-05-19 A. K. Kwasniewski

We consider the problem of distributed statistical machine learning in adversarial settings, where some unknown and time-varying subset of working machines may be compromised and behave arbitrarily to prevent an accurate model from being…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-10-24 Yudong Chen , Lili Su , Jiaming Xu

Standard federated learning algorithms are vulnerable to adversarial nodes, a.k.a. Byzantine failures. To solve this issue, robust distributed learning algorithms have been developed, which typically replace parameter averaging by robust…

Machine Learning · Computer Science 2026-02-04 Renaud Gaucher , Aymeric Dieuleveut , Hadrien Hendrikx

Traditionally, Byzantine fault tolerance (BFT) in geo-replicated systems is achieved by executing complex agreement protocols over large-distance communication links, and therefore typically incurs high response times. In this paper we…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-12 Michael Eischer , Tobias Distler

Distributed ledgers are increasingly relied upon by industry to provide trustworthy accountability, strong integrity protection, and high availability for critical data without centralizing trust. Recently, distributed append-only logs are…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-02-06 Shubham Mishra , João Gonçalves , Chawinphat Tankuranand , Neil Giridharan , Natacha Crooks , Heidi Howard , Chris Jensen

We study Exo-MDPs, a structured class of Markov Decision Processes (MDPs) where the state space is partitioned into exogenous and endogenous components. Exogenous states evolve stochastically, independent of the agent's actions, while…

Machine Learning · Statistics 2025-02-06 Jia Wan , Sean R. Sinclair , Devavrat Shah , Martin J. Wainwright

A variety of possible extensions of mappings between posets to their Dedekind order completion is presented. One of such extensions has recently been used for solving large classes of nonlinear systems of partial differential equations with…

General Mathematics · Mathematics 2007-05-23 Elemer E Rosinger

Traditionally, security systems for enterprises have implicit access based on strong cryptography, authentication and key sharing, wherein access control is based on Role Based Access Control (RBAC), in which roles such as manager,…

Cryptography and Security · Computer Science 2022-03-18 Nitesh Kumar , Gaurav S. Kasbekar , D. Manjunath

This paper aims at jointly addressing two seemly conflicting issues in federated learning: differential privacy (DP) and Byzantine-robustness, which are particularly challenging when the distributed data are non-i.i.d. (independent and…

Machine Learning · Computer Science 2022-08-03 Heng Zhu , Qing Ling

Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the…

Machine Learning · Computer Science 2020-07-07 Zhixiong Yang , Waheed U. Bajwa

Jointly addressing Byzantine attacks and privacy leakage in distributed machine learning (DML) has become an important issue. A common strategy involves integrating Byzantine-resilient aggregation rules with differential privacy mechanisms.…

Machine Learning · Computer Science 2025-06-19 Bing Liu , Chengcheng Zhao , Li Chai , Peng Cheng , Yaonan Wang

We propose three new robust aggregation rules for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the…

Distributed, Parallel, and Cluster Computing · Computer Science 2018-03-28 Cong Xie , Oluwasanmi Koyejo , Indranil Gupta

PermitBFT establishes a permissioned byzantine ledger in the partially synchronous networking model. For n replicas, PermitBFT tolerates up to f < n/3 byzantine replicas. It is the first BFT protocol to achieve a latency of just 2 message…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-11-03 Roland Schmid , Roger Wattenhofer

Many applications model their data in a general-purpose storage format such as JSON. This data structure is modified by the application as a result of user input. Such modifications are well understood if performed sequentially on a single…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-08-16 Martin Kleppmann , Alastair R. Beresford

The Entity-Relationship (ER) model is widely used for creating ER schemas for modeling application domains in the field of Information Systems development. However, when an ER schema is transformed to a Relational Database Schema (RDS),…

Software Engineering · Computer Science 2020-03-02 Dhammika Pieris , M. C Wijegunesekera , N. G. J. Dias

In this paper, we present a Byzantine fault tolerant distributed commit protocol for transactions running over untrusted networks. The traditional two-phase commit protocol is enhanced by replicating the coordinator and by running a…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-11-17 Wenbing Zhao

Extended Dynamic Mode Decomposition (EDMD) is a widely-used data-driven approach to learn an approximation of the Koopman operator. Consequently, it provides a powerful tool for data-driven analysis, prediction, and control of nonlinear…

Systems and Control · Electrical Eng. & Systems 2024-08-23 Yang Guo , Manuel Schaller , Karl Worthmann , Stefan Streif

Safety-critical controllers of complex systems are hard to construct manually. Automated approaches such as controller synthesis or learning provide a tempting alternative but usually lack explainability. To this end, learning decision…

Artificial Intelligence · Computer Science 2025-03-26 Debraj Chakraborty , Clemens Dubslaff , Sudeep Kanav , Jan Kretinsky , Christoph Weinhuber