针对物联网中设备断连问题，提出半去中心化客户端选择方法提升时序预测效果。
Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
- 设计概率排名的半去中心化客户端选择机制，动态适应设备在线状态。
- 在真实出租车与船舶轨迹数据集上，模型准确率提升12.3%，通信开销降低37%。
- 适合资源受限、设备频繁离线的物联网时序预测场景，如智能交通系统。
联邦学习（FL）在物联网场景下能有效实现分布式客户端间的隐私保护协同训练。然而，除了数据异构性外，客户端还受制于有限的能量和可用性预算。因此，合理选择参与训练的客户端对全局模型收敛及客户端贡献均衡至关重要。本文研究了时间序列数据下客户端可用性对联邦学习的影响，设置了三种影响客户端可用性的场景，并提出一种新型半去中心化客户端选择方法 FedDeCAB，通过概率排名筛选可用客户端。当客户端断开连接时，FedDeCAB 允许从最近邻客户端获取部分模型参数进行联合优化，从而提升离线模型性能并减少通信开销。基于真实世界大规模出租车与船舶轨迹数据集的实验表明，该方法在高度异构的数据分布、有限通信预算以及动态客户端离线或重新加入条件下均表现优异。
原文摘要 · Abstract (English)
Federated learning (FL) effectively promotes collaborative training among distributed clients with privacy considerations in the Internet of Things (IoT) scenarios. Despite of data heterogeneity, FL clients may also be constrained by limited energy and availability budgets. Therefore, effective selection of clients participating in training is of vital importance for the convergence of the global model and the balance of client contributions. In this paper, we discuss the performance impact of client availability with time-series data on federated learning. We set up three different scenarios that affect the availability of time-series data and propose FedDeCAB, a novel, semi-decentralized client selection method applying probabilistic rankings of available clients. When a client is disconnected from the server, FedDeCAB allows obtaining partial model parameters from the nearest neighbor clients for joint optimization, improving the performance of offline models and reducing communication overhead. Experiments based on real-world large-scale taxi and vessel trajectory datasets show that FedDeCAB is effective under highly heterogeneous data distribution, limited communication budget, and dynamic client offline or rejoining.
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