Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
Pfedmoe: Data-level personalization with mixture of experts for model-heterogeneous personalized federated learning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
FedCoE proposes a coordinated dual-level MoE framework for federated learning that improves global and personalized accuracy while enabling strong cold-start performance for new clients.
A new framework trains personal digital health models using adaptive weights on support users including dissimilar ones, achieving up to 25% lower RMSE in low-data settings.
FLEX-MoE proposes client-expert fitness scores and an optimization algorithm to jointly maximize specialization and enforce balanced expert utilization in federated MoE for edge computing under non-IID data and capacity constraints.
citing papers explorer
-
Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
-
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
FedCoE proposes a coordinated dual-level MoE framework for federated learning that improves global and personalized accuracy while enabling strong cold-start performance for new clients.
-
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
FLEX-MoE proposes client-expert fitness scores and an optimization algorithm to jointly maximize specialization and enforce balanced expert utilization in federated MoE for edge computing under non-IID data and capacity constraints.