Search Results for author:
Found 5 papers, 2 papers with code
Federated Learning with Incomplete Sensing Modalities
To address these issues, we introduce FLISM, a framework designed to enable multimodal FL with incomplete modalities.
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
(3) However, algorithms such as FedDyn and SCAFFOLD are more prone to catastrophic failures without the support of additional techniques such as gradient clipping.
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Psychiatrists diagnose mental disorders via the linguistic use of patients.
Flame: Simplifying Topology Extension in Federated Learning
We present Flame, a new system that provides flexibility of the topology configuration of distributed FL applications around the specifics of a particular deployment context, and is easily extensible to support new FL architectures.
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
