445 citations · 830 across the 60 of their papers we have counts for
4 papers · 1 filter
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.…
A Unified Analysis of Federated Learning with Arbitrary Client Participation
Shiqiang Wang, Mingyue Ji
Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate i…
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
Won Joon Yun, Yunseok Kwak, Hankyul Baek +5
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is chal…
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…