222 citations · 465 across the 15 of their papers we have counts for
5 papers · 1 filter
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Su Wang, Roberto Morabito, Seyyedali Hosseinalipour +2
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Su Wang, Rajeev Sahay, Adam Piaseczny +1
Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversaria…
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation
Su Wang, Seyyedali Hosseinalipour, Christopher G. Brinton
Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimension…
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal +4
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely ne…
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Su Wang, Rajeev Sahay, Christopher G. Brinton
There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating…