Air quality prediction utilizes IoT technologies to collect data centrally for model training, which may cause regulatory risks, privacy concerns, and high costs of integrating data. Meanwhile, distributed training through federated learning relies on the pre-defined graph structure generated by the geographic locations of air monitoring stations, which fails to capture the potential spatial relationships between air monitoring stations. In addition, the inherent multi-period attribute of air quality data makes time series changes extremely complex. To this end, this paper proposes a Federated framework for air quality prediction with Pre-defined graph and Adaptive Graph (FedPAG). Specifically, the client encodes the air quality data and meteorological data provided by the Internet of Things (IoT) system using the multi-period interaction and encoder modules to capture the proximity and periodicity features of the time series data. Next, the server combines the hidden states uploaded by the clients with pre-defined graph and adaptive graph respectively and forms node embeddings to capture the spatial features among the clients. Then, the client concatenates the hidden states with node embeddings to fuse the spatial information and feeds them into the decoder to obtain the final predicted values. Finally, we conduct experiments on the Beijing and Shijiazhuang datasets to demonstrate the effectiveness of the proposed method.

The cloud computing simplifies application development into the orchestration of virtual-services workflow. However, network latency between geographically distributed hosts would slow down the workflow's makespan time. This paper proposes a geo-aware task allocation approach (GTAA). GTAA partitions the workflow for geo-distributed data centers(DCs) and reduces sub-workflows across DCs. GTAA aims to optimize overall workflow makespan time and improves the efficiency of workflow.