3 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
Hash & Adjust: Competitive Demand-Aware Consistent Hashing
Arash Pourdamghani, Chen Avin, Robert Sama +2
Distributed systems often serve dynamic workloads and resource demands evolve over time. Such a temporal behavior stands in contrast to the static and demand-oblivious nature of mo…
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…