6 citations · 6 across the 6 of their papers we have counts for
6 papers
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
Jake B. Perazzone, Shiqiang Wang, Mingyue Ji +1
Federated learning (FL) is a useful tool that enables the training of machine learning models over distributed data without having to collect data centrally. When deploying FL in c…
Joint Task Offloading and Routing in Wireless Multi-hop Networks Using Biased Backpressure Algorithm
Zhongyuan Zhao, Jake Perazzone, Gunjan Verma +3
A significant challenge for computation offloading in wireless multi-hop networks is the complex interaction among traffic flows in the presence of interference. Existing approache…
Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Zhongyuan Zhao, Jake Perazzone, Gunjan Verma +1
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices. Existing offloading schemes mainly focus on mobile devices and servers,…
Learning to Transmit with Provable Guarantees in Wireless Federated Learning
Boning Li, Jake Perazzone, Ananthram Swami +1
We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challen…
Federated Learning with Flexible Control
Shiqiang Wang, Jake Perazzone, Mingyue Ji +1
Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g.…
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization
Jake Perazzone, Shiqiang Wang, Mingyue Ji +1
Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wire…