Search Results for author:
Found 8 papers, 0 papers with code
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs with on-device local data.
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
For both cross-device and cross-silo settings, we show that FedLabel outperforms other semi-supervised FL baselines by $8$-$24\%$, and even outperforms standard fully supervised FL baselines ($100\%$ labeled data) with only $5$-$20\%$ of labeled data.
On the Convergence of Federated Averaging with Cyclic Client Participation
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL).
Maximizing Global Model Appeal in Federated Learning
We provide convergence guarantees for MaxFL and show that MaxFL achieves a $22$-$40\%$ and $18$-$50\%$ test accuracy improvement for the training clients and unseen clients respectively, compared to a wide range of FL modeling approaches, including those that tackle data heterogeneity, aim to incentivize clients, and learn personalized or fair models.
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server.
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer
Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round.
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing.
