FedQueue predicts per-facility queue delays, applies cutoff admission to bound staleness, and uses staleness-aware aggregation, yielding O(1/sqrt(R)) convergence for non-convex objectives and up to 60% faster time-to-target-accuracy in simulations and 20.5% real-world improvement.
When foundation model meets federated learning: Motivations, challenges, and future directions
4 Pith papers cite this work. Polarity classification is still indexing.
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FedRouter clusters adapters locally per task samples and globally across clients to create task-centric personalized models, improving generalization and reducing task interference in federated fine-tuning.
A survey of personalization techniques and foundation model adaptations in federated settings for privacy-preserving recommendations, emphasizing their architectural intersection.
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
citing papers explorer
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FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
FedQueue predicts per-facility queue delays, applies cutoff admission to bound staleness, and uses staleness-aware aggregation, yielding O(1/sqrt(R)) convergence for non-convex objectives and up to 60% faster time-to-target-accuracy in simulations and 20.5% real-world improvement.
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Task-Centric Personalized Federated Fine-Tuning of Language Models
FedRouter clusters adapters locally per task samples and globally across clients to create task-centric personalized models, improving generalization and reducing task interference in federated fine-tuning.
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A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
A survey of personalization techniques and foundation model adaptations in federated settings for privacy-preserving recommendations, emphasizing their architectural intersection.
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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.