6 papers
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…
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…
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…
Time and Space Optimal Massively Parallel Algorithm for the 2-Ruling Set Problem
Mélanie Cambus, Fabian Kuhn, Shreyas Pai +1
In this work, we present a constant-round algorithm for the -ruling set problem in the Congested Clique model. As a direct consequence, we obtain a constant round algorithm in t…
Massively Parallel Correlation Clustering in Bounded Arboricity Graphs
Mélanie Cambus, Davin Choo, Havu Miikonen +1
Identifying clusters of similar elements in a set is a common task in data analysis. With the immense growth of data and physical limitations on single processor speed, it is neces…