FLEX-MoE：用于边缘计算的联邦混合专家模型，具有负载平衡的专家分配
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
AI总结：
本文提出FLEX-MoE，一种联邦混合专家框架，通过联合优化专家分配和负载平衡，在受限客户端容量下提升边缘计算性能。
摘要：
混合专家（MoE）模型通过条件计算实现可扩展的神经网络，为下一代无线通信提供增强的有效性和效率。然而，在无线和物联网边缘网络上使用联邦学习（FL）部署MoE面临两个关键挑战：1）资源受限的客户端无法存储具有完整专家集的大型AI模型；2）非独立同分布（non-IID）数据分布导致严重的专家负载不平衡，从而降低模型性能。为此，我们提出了FLEX-MoE，一种联邦MoE框架，该框架在有限的客户端容量下联合优化专家分配和负载平衡。具体而言，我们的方法引入了客户端-专家适应度分数，通过训练反馈量化专家对本地数据集的适用性，并采用基于优化的算法来最大化客户端-专家专业化，同时确保系统范围内的负载平衡。与仅关注个性化而忽略负载不平衡的贪心方法不同，FLEX-MoE解决了专家利用偏斜问题，这在异构边缘FL中尤为严重。我们的实验结果表明，在各种资源受限场景下，FLEX-MoE在准确性和一致的负载平衡方面均表现优异。
英文摘要：
Mixture-of-Experts (MoE) models enable scalable neural networks through conditional computation, offering enhanced effectiveness and efficiency for next-generation wireless communications. However, deploying MoE with federated learning (FL) over wireless and IoT edge networks faces two critical challenges: 1) resource-constrained clients cannot store large AI models with full expert sets, and 2) non-IID data distributions cause severe expert load imbalance that degrades model performance. To this end, we propose FLEX-MoE, a federated MoE framework that jointly optimizes expert assignment and load balancing under limited client capacity. Specifically, our approach introduces client-expert fitness scores that quantify expert suitability for local datasets through training feedback, and employs an optimization-based algorithm to maximize client-expert specialization while enforcing balanced expert utilization system-wide. Unlike greedy methods that focus solely on personalization while ignoring load imbalance, FLEX-MoE addresses expert utilization skew, which is particularly severe in heterogeneous edge FL. Our experimental results demonstrate superior accuracy and consistently balanced expert utilization across diverse resource-constrained scenarios for edge computing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
