FedMoE: Personalized Federated Learning via Heterogeneous Mixture of
Experts

FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

21 August 2024

Shangguang Wang

Mengwei Xu

Papers citing "FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts"

5 / 5 papers shown

Title
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Jiacheng Wang Hongtao Lv Lei Liu FedML 25 0 0 10 May 2025
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness Zihao Zheng Xiuping Cui Size Zheng Maoliang Li Jiayu Chen Yun Liang Xiang Chen MQ MoE 69 0 0 27 Mar 2025
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures Yicheng Zhang Zhen Qin Zhaomin Wu Jian Hou Shuiguang Deng 80 2 0 28 Nov 2024
Scalable and Efficient MoE Training for Multitask Multilingual Models Young Jin Kim A. A. Awan Alexandre Muzio Andres Felipe Cruz Salinas Liyang Lu Amr Hendy Samyam Rajbhandari Yuxiong He Hany Awadalla MoE 104 84 0 22 Sep 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 264 4,505 0 23 Jan 2020