FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression
This paper introduces FairCompressAgent (FCA), a novel agentic framework designed to handle the complexities of model compression while maintaining fairness and minimizing resource usage. The authors propose integrating various techniques such as fairness-aware pruning, incremental quantization, and sparse low-rank factorization through a unified interface. FCA uses a language-model planner that…
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Key points
- Proposes FairCompressAgent (FCA) for fair model compression
- Integrates multiple techniques including pruning, quantization, and factorization
- Supports dynamic requirement updates and constraint-based selection
Read the original at arXiv cs.AI · by Yuanbo Guo, Yiyu Shi primary source Open source ↗
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