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cs.LG2025
Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?
Xuanyu Chen, Nan Yang, Shuai Wang +1
The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about t…
cs.LG2025
Memory-Efficient Fine-Tuning via Low-Rank Activation Compression
Jiang-Xin Shi, Wen-Da Wei, Jin-Fei Qi +3
The parameter-efficient fine-tuning paradigm has garnered significant attention with the advancement of foundation models. Although numerous methods have been proposed to reduce th…