Javad Parsa — accepted AI-conference papers

2026

Byzantine-Robust Federated Learning with Learnable Aggregation Weights

Javad Parsa, Amir Hossein Daghestani, André M. H. Teixeira, Mikael Johansson

ICLR 2026poster

Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicious (Byzantine) clients poses significant challenges to the robustness of FL, particularly when data distributions across clients are heterogeneous. In t…

Cited by 0 Source ↗Scholar ↗

2020

Low Mutual and Average Coherence Dictionary Learning Using Convex Approximation

Javad Parsa, Mostafa Sadeghi, Massoud Babaie-Zadeh, Christian Jutten

ICASSP 2020accepted

In dictionary learning, a desirable property for the dictionary is to be of low mutual and average coherences. Mutual coherence is defined as the maximum absolute correlation between distinct atoms of the dictionary, whereas the average coherence is a measure of the average correlations. In this pap…

Cited by 3 Source ↗Scholar ↗