3 citations · 3 across the 12 of their papers we have counts for
13 papers
Faster Convergence of Multidimensional Approximate Agreement via Smallest Enclosing Balls
Darya Melnyk
This work considers the multidimensional approximate agreement problem. In this problem, parties in a distributed system, up to of which may be corrupted by a Byzantine adv…
Privacy Attacks on Stable Marriage
Stephan A. Fahrenkrog-Petersen, Aleksander Figiel, Darya Melnyk +2
The stable marriage problem appears in many privacy-sensitive domains, for example in the National Resident Matching Program in the US. In such applications, preserving the privacy…
Resilient Byzantine Agreement with Predictions
Julien Dallot, Darya Melnyk, Tijana Milentijevic +2
This paper studies the Byzantine Agreement problem where the nodes have access to a predictor that flags nodes for suspicion of faulty (Byzantine) behavior. We focus on algorithmic…
Online Graph Embedding in Star Graphs
Julien Dallot, Darya Melnyk, Maciej Pacut +1
Graph embedding is a fundamental problem of mapping nodes of a guest graph into a host graph while minimizing the distance distortion, with broad applications, including virtual ne…
Practical Validity Conditions for Byzantine-Tolerant Federated Learning
Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1
Robust aggregation is the core operation in Byzantine-tolerant federated learning. To ensure the quality of aggregation independently of data distribution or attacks, validity cond…
The Carrier Pigeon Internet Protocol: An Algorithmic (and Lighthearted) Perspective
Matthias Bentert, Shay Kutten, Darya Melnyk +2
The theoretical model behind the pigeon post as a link layer in a communication network was introduced by Shannon (under the guise of studying One-Time Pads for cryptography). That…
BSB: Towards Demand-Aware Peer Selection With XOR-based Routing
Qingyun Ji, Darya Melnyk, Arash Pourdamghani +1
Peer-to-peer networks, as a key enabler of modern networked and distributed systems, rely on peer-selection algorithms to optimize their scalability and performance. Peer-selection…
Centroid Approximation for Byzantine-Tolerant Federated Learning
Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1
Federated learning allows each client to keep its data locally when training machine learning models in a distributed setting. Significant recent research established the requireme…
Approximate Agreement Algorithms for Byzantine Collaborative Learning
Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1
In Byzantine collaborative learning, clients in a peer-to-peer network collectively learn a model without sharing their data by exchanging and aggregating stochastic gradient e…
SpiderDAN: Matching Augmentation in Demand-Aware Networks
Aleksander Figiel, Darya Melnyk, André Nichterlein +2
Graph augmentation is a fundamental and well-studied problem that arises in network optimization. We consider a new variant of this model motivated by reconfigurable communication…
DecentPeeR: A Self-Incentivised & Inclusive Decentralized Peer Review System
Johannes Gruendler, Darya Melnyk, Arash Pourdamghani +1
Peer review, as a widely used practice to ensure the quality and integrity of publications, lacks a well-defined and common mechanism to self-incentivize virtuous behavior across a…
Learning Minimum Linear Arrangement of Cliques and Lines
Julien Dallot, Maciej Pacut, Marcin Bienkowski +2
In the well-known Minimum Linear Arrangement problem (MinLA), the goal is to arrange the nodes of an undirected graph into a permutation so that the total stretch of the edges is m…
Centroid Approximation with Multidimensional Approximate Agreement Protocols
Melanie Cambus, Darya Melnyk
In this paper, we present distributed fault-tolerant algorithms that approximate the centroid (i.e., the average) of a set of data points in . Our work falls into…