works on

From the 1 of 8 linked papers with an AI index.

activity
20242026
collaborators

8 papers

cs.DS2026

Privacy Attacks on Stable Marriage

Stephan A. Fahrenkrog-Petersen, Aleksander Figiel, Darya Melnyk +2

The paper shows that repeated interactions with the Gale‑Shapley stable marriage algorithm can leak the private preference lists of participants, and it identifies conditions under…

cs.LG2026

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…

cs.NI2025

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…

cs.LG2025

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…

cs.LG2025

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…

cs.DC2024

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…