3 citations · 5 across the 6 of their papers we have counts for
6 papers
Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
Ehsan Lari, Reza Arablouei, Stefan Werner
We propose a Byzantine-resilient federated conformal prediction (FCP) method that leverages partial model sharing, where only a subset of model parameters is exchanged each round.…
Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
Ehsan Lari, Reza Arablouei, Stefan Werner
We propose PRISM-FCP (Partial shaRing and robust calIbration with Statistical Margins for Federated Conformal Prediction), a communication-efficient Byzantine-robust federated conf…
Noise-Robust and Resource-Efficient ADMM-based Federated Learning
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To ad…
Privacy-Preserving Distributed Nonnegative Matrix Factorization
Ehsan Lari, Reza Arablouei, Stefan Werner
Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a de…
Distributed Maximum Consensus over Noisy Links
Ehsan Lari, Reza Arablouei, Naveen K. D. Venkategowda +1
We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of no…
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicio…