Search Results for author:
Found 6 papers, 0 papers with code
Concept Matching: Clustering-based Federated Continual Learning
To avoid interference among client models with different concepts, the server clusters the models representing the same concept, aggregates the model weights in each cluster, and updates the global concept model with the cluster model of the same concept.
Zone-based Federated Learning for Mobile Sensing Data
However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy.
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning
For improved model performance, these two types of complementary sparse models are aggregated into a dense model in each round, which is subsequently pruned in an iterative process.
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps.
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users
The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system.
Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform
Applying Machine Learning (ML) to business applications for automation usually faces difficulties when integrating diverse ML dependencies and services, mainly because of the lack of a common ML framework.
