arXiv Machine Learning By Fatemeh Nourzad, Amirhossein Roknilamouki, Eylem Ekici, Jia Liu, Ness Shroff

FIRM: Federated In-client Regularized Multi-objective Alignment for Large Language Models

Read the original on arXiv Machine Learning →

arXiv:2511. 16992v3 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with human values often involves balancing multiple, conflicting objectives such as helpfulness and harmlessness.

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arXiv Machine Learning
Jun 16

Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts

arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.

By Yijun Lu, Zihan Fang, Pengpeng Qiao, Zheng Lin, Jing Yang, Yuxin Zhang, Por Lip Yee, Zhe Chen, Jun Luo