这篇论文用空间聚类和驻留时间自适应平均，解决了移动场景下联邦学习数据漂移的老大难问题，效果比标准FL好很多。
研究者提出C2FL，一种完全分布式的联邦学习方法。节点通过空间聚类自组织成学习组，反映环境地理结构。为应对时间漂移，每个节点结合经验回放与驻留时间感知的自适应平均步骤，逐步融入区域共识。合成实验表明，标准联邦学习策略在空间和时间漂移下准确率下降超过30%，而C2FL恢复至接近集中式训练的性能。
C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment. Scaling this intelligence, however, raises fundamental challenges: sensed data is often privacy-sensitive, preventing centralized collection; nodes are mobile, traversing regions where nearby nodes perceive similar phenomena while distant ones observe radically different conditions, creating natural spatial clusters; and these distributions evolve over time due to mobility, introducing temporal drift that makes local models progressively stale. These dynamics arise across domains - vehicular sensing, drone-based monitoring, smartphone crowdsensing - yet the interplay of privacy, spatial heterogeneity, and temporal drift severely undermines conventional learning strategies. Therefore, we propose C2FL, a fully distributed Federated Learning (FL) approach where nodes self-organize into learning groups through spatial clustering, reflecting the geographic structure of the environment. To counteract temporal drift, each node combines experience replay with a dwell-time-aware adaptive averaging step, progressively incorporating the regional consensus as it remains longer within the same area, while preserving previously acquired knowledge under evolving distributions. We evaluate our approach on synthetic experiments that systematically reproduce spatial and temporal shifts, showing that standard federated strategies degrade significantly under these conditions and that our method restores robust collective adaptation.