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Found 3 papers, 1 papers with code
A Survey on Contribution Evaluation in Vertical Federated Learning
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing.
Adapting LLMs for Efficient Context Processing through Soft Prompt Compression
By amalgamating soft prompt compression with sophisticated summarization, SoftPromptComp confronts the dual challenges of managing lengthy contexts and ensuring model scalability.
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system.
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