Submodel partitioning in hierarchical federated learning: Algorithm design and convergence analysis
Hierarchical federated learning (HFL) has demon-strated promising scalability advantages
over the traditional “star-topology” architecture-based federated learning (FL). How-ever …
over the traditional “star-topology” architecture-based federated learning (FL). How-ever …
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
The interest in federated learning has surged in recent research due to its unique ability to
train a global model using privacy-secured information held locally on each client. This …
train a global model using privacy-secured information held locally on each client. This …
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
In federated learning (FL), accommodating clients' varied computational capacities poses a
challenge, often limiting the participation of those with constrained resources in global …
challenge, often limiting the participation of those with constrained resources in global …
Review and Comparative Evaluation of Resource-Adaptive Collaborative Training for Heterogeneous Edge Devices
Growing concerns about centralized mining of personal data threatens to stifle further
proliferation of machine learning (ML) applications. Consequently, a recent trend in ML …
proliferation of machine learning (ML) applications. Consequently, a recent trend in ML …
MAST: Model-Agnostic Sparsified Training
We introduce a novel optimization problem formulation that departs from the conventional
way of minimizing machine learning model loss as a black-box function. Unlike traditional …
way of minimizing machine learning model loss as a black-box function. Unlike traditional …
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models
J Xie, S Li - arXiv preprint arXiv:2407.03622, 2024 - arxiv.org
Training large models requires a large amount of data, as well as abundant computation
resources. While collaborative learning (eg, federated learning) provides a promising …
resources. While collaborative learning (eg, federated learning) provides a promising …
MSfusion: Enabling Collaborative Training of Large Models over Resource-Constraint Participants
J Xie, S Li - openreview.net
Training large models like GPT-3 requires a large amount of data, as well as abundant
computation resources. While collaborative learning (eg, federated learning) provides a …
computation resources. While collaborative learning (eg, federated learning) provides a …