4 papers
RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang +1
Machine learning (ML) algorithms deployed in real-world environments are often faced with the challenge of adapting models to concept drift, where the task data distributions are s…
Adversarial Node Placement in Decentralized Federated Learning: Maximum Spanning-Centrality Strategy and Performance Analysis
Adam Piaseczny, Eric Ruzomberka, Rohit Parasnis +1
As Federated Learning (FL) becomes more widespread, there is growing interest in its decentralized variants. Decentralized FL leverages the benefits of fast and energy-efficient de…
Computation and Communication Co-scheduling for Multi-Task Remote Inference
Md Kamran Chowdhury Shisher, Adam Piaseczny, Yin Sun +1
In multi-task remote inference systems, an intelligent receiver (e.g., command center) performs multiple inference tasks (e.g., target detection) using data features received from…
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…