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
5 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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UNVERDICTED 5representative 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.
FedFMX adds Fisher-routed expert selection and routing-aware regularization to federated class-incremental learning and proves an O(T^{-1}) convergence rate.
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
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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.
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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.
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Personalized Digital Health Modeling with Adaptive Support Users
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.
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Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
FedFMX adds Fisher-routed expert selection and routing-aware regularization to federated class-incremental learning and proves an O(T^{-1}) convergence rate.
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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.