发表机构
ENSIAS, Mohammed V University in Rabat; University of Quebec at Trois-Rivieres（拉巴特穆罕默德五世大学ENSIAS; 魁北克大学三河城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构IoT中联邦学习统计异质性，提出基于HK意见动态的联盟形成方法，在局部权重空间形成联盟聚合，降低MAE最高54%并提升全局准确率。
AI 中文摘要
联邦学习（FL）能够在异构物联网（IoT）部署（如智慧城市水表计量网络）中实现隐私保护、设备端训练，其中每个智能水表观测一个家庭特定的消费时间序列。在这种统计异质性下，标准联邦平均（FedAvg）聚合算法将不同的局部模型平均为一个全局模型，该模型可能无法捕捉客户端特定的模式。我们通过在局部权重空间中直接形成客户端联盟并在联盟层面进行聚合来解决这一问题。扩展先前基于权重的联盟形成方案，我们将联盟形成建模为作用于局部权重的Hegselmann-Krause（HK）有界置信意见动态过程，并开发了基于欧氏距离和余弦相似度置信准则的HK交互变体。该框架应用于使用局部长短期记忆（LSTM）模型的短期用水量预测，并与FedAvg、Per-FedAvg、FedProx以及采用欧氏距离或余弦相似度联盟形成的FedAvg进行对比评估。在真实的水消耗智能计量数据集上的实验表明，所提出的基于HK的联盟形成在至多十次内部迭代内产生稳定、内生的联盟结构，与FedAvg相比不增加客户端计算或通信开销，相对于FedAvg平均MAE降低高达54%，相对于FedProx降低39%，相对于Per-FedAvg降低24%，同时实现了最高的全局准确率（83-85%）。
英文摘要
Federated learning (FL) enables privacy-preserving, on-device training across heterogeneous Internet-of-Things (IoT) deployments such as smart-city water-metering networks, where each smart meter observes a household-specific consumption time series. Under such statistical heterogeneity, the standard Federated Averaging (FedAvg) aggregation averages dissimilar local models into a single global model that may fail to capture client-specific patterns. We address this by forming client coalitions directly in the local-weight space and aggregating at the coalition level. Extending a prior weight-driven coalition-formation scheme, we model coalition formation as a Hegselmann-Krause (HK) bounded-confidence opinion-dynamics process acting on the local weights, and develop variants of the HK interaction based on Euclidean-distance and cosine-similarity confidence criteria. The framework is applied to short-term water-consumption forecasting with local Long Short-Term Memory (LSTM) models and evaluated against FedAvg, Per-FedAvg, FedProx, and FedAvg with Euclidean-distance or cosine-similarity coalition formation. Experiments on a real smart-metering dataset of water consumption show that the proposed HK-based coalition formation produces stable, endogenous coalition structures within at most ten inner iterations, incurs no additional client-side computation or communication compared to FedAvg, and reduces the average MAE by up to 54% relative to FedAvg, 39% relative to FedProx, and 24% relative to Per-FedAvg, while achieving the highest global accuracy (83-85%).
