针对移动集群频繁变化导致的模型失效问题，提出新框架提升联邦学习稳定性。
MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network
- 用相似度矩阵动态聚合客户端特征，适应节点频繁变动。
- 融合历史与当前特征训练全局模型，减少灾难性遗忘。
- 适合高动态移动网络场景，性能稳定且开销可控。
在高度动态的移动集群中，客户端节点频繁变化会导致特征分布和数据漂移的显著变化，严重挑战现有联邦学习策略的鲁棒性。为此，我们提出了移动集群联邦学习框架（MoCFL）。MoCFL通过引入亲和力矩阵来量化不同客户端局部特征提取器间的相似性，增强特征聚合能力，以应对由频繁客户端加入退出和拓扑变化引发的数据分布动态变化。同时，MoCFL在训练全局分类器时融合历史与当前特征信息，有效缓解移动场景中常见的灾难性遗忘问题。该协同机制确保了MoCFL在动态变化的移动环境中保持高性能与稳定性。在UNSW-NB15数据集上的实验结果表明，MoCFL在动态环境下表现出色，兼具优异的鲁棒性和准确性，同时维持合理的训练成本。
原文摘要 · Abstract (English)
Frequent fluctuations of client nodes in highly dynamic mobile clusters can lead to significant changes in feature space distribution and data drift, posing substantial challenges to the robustness of existing federated learning (FL) strategies. To address these issues, we proposed a mobile cluster federated learning framework (MoCFL). MoCFL enhances feature aggregation by introducing an affinity matrix that quantifies the similarity between local feature extractors from different clients, addressing dynamic data distribution changes caused by frequent client churn and topology changes. Additionally, MoCFL integrates historical and current feature information when training the global classifier, effectively mitigating the catastrophic forgetting problem frequently encountered in mobile scenarios. This synergistic combination ensures that MoCFL maintains high performance and stability in dynamically changing mobile environments. Experimental results on the UNSW-NB15 dataset show that MoCFL excels in dynamic environments, demonstrating superior robustness and accuracy while maintaining reasonable training costs.
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