ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge

Ali Nemati9 hours ago25 sec read2 views

ELSA is a new framework that integrates split learning and hierarchical federated learning to train large language models at the network edge while addressing challenges like data heterogeneity and privacy risks. It introduces client clustering, dynamic model splitting, and a lightweight communication scheme to enhance efficiency and scalability for content creators working with resource-constrained devices.

Read the full article at arXiv cs.LG (ML)

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