5 citations · 22 across the 17 of their papers we have counts for
15 papers · 1 filter
Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
Hasan Burhan Beytur, Haris Vikalo, Kevin S Chan +1
We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provi…
Batching-Aware Joint Model Onloading and Offloading for Hierarchical Multi-Task Inference
Seohyeon Cha, Kevin Chan, Gustavo de Veciana +1
The growing demand for intelligent services on resource-constrained edge devices has spurred the development of collaborative inference systems that distribute workloads across end…
Transformers as Implicit State Estimators: In-Context Learning in Dynamical Systems
Usman Akram, Haris Vikalo
Predicting the behavior of a dynamical system from noisy observations of its past outputs is a classical problem encountered across engineering and science. For linear systems with…
Recovering Labels from Local Updates in Federated Learning
Huancheng Chen, Haris Vikalo
Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients' data from communicated mode…
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity
Yiyue Chen, Haris Vikalo, Chianing Wang
Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies o…
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
Huancheng Chen, Haris Vikalo
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precisi…