FedCoLLM is a parameter-efficient federated co-tuning framework that improves client SLMs via server LLMs and enriches LLMs with client domain insights using adapters on NLP text generation tasks.
In: Artificial intelligence and statistics
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FLAIR combines LEACH-style dynamic clustering with in-cluster federated learning, achieving competitive accuracy and resilience in simulated sensor networks.
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Federated Co-tuning Framework for Large and Small Language Models
FedCoLLM is a parameter-efficient federated co-tuning framework that improves client SLMs via server LLMs and enriches LLMs with client domain insights using adapters on NLP text generation tasks.
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FLAIR: Distributed Federated Learning with Dynamic Clustering
FLAIR combines LEACH-style dynamic clustering with in-cluster federated learning, achieving competitive accuracy and resilience in simulated sensor networks.