140 citations · 293 across the 25 of their papers we have counts for
5 papers · 1 filter
Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning
Connor Mclaughlin, Matthew Ding, Denis Edogmus +1
As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communicati…
Fast and Robust State Estimation and Tracking via Hierarchical Learning
Connor Mclaughlin, Matthew Ding, Deniz Erdogmus +1
Fast and reliable state estimation and tracking are essential for real-time situation awareness in Cyber-Physical Systems (CPS) operating in tactical environments or complicated ci…
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via minimizing a global obje…
Federated Learning in the Presence of Adversarial Client Unavailability
Lili Su, Ming Xiang, Jiaming Xu +1
Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data. Due to the diverse hardware and software limi…
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles
Muzi Peng, Jiangwei Wang, Dongjin Song +2
Deep learning is the method of choice for trajectory prediction for autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires the availability of sufficiently…