3 citations · 3 across the 12 of their papers we have counts for
3 papers · 1 filter
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…