LAUSR

The advancements of AI and the exponential growth of sensory data are unlocking a wave of intelligent sensing applications. To overcome the shortcoming of centralized learning and local training, Google proposes federated learning that allows users to collectively reap the benefits of shared models trained from decentralized data. However, directly applying federated learning to intelligent sensing applications faces two deficiencies: 1) omitting personalities of local models and 2) high latency. Aiming at these limitations, in this article, we propose a new framework, Federated Sensing, to enable edge-cloud elastic collaborative learning from decentralized sensory data. We design an elastic local update algorithm that can train the personalized models by setting specific updating weights for each node based on the difference between the global and local model. Our algorithm takes both the global consistency and the personalities of the local models. We further propose an $n$ -softsync model aggregation method that significantly reduces training time by combining the synchronous and asynchronous aggregations. Extensive experiments are conducted on two real-world data sets of air quality from Beijing and Los Angeles. Compared with existing federated learning techniques, our framework improves the model performance at least by 2.61% and 18.8% in two data sets, respectively. Besides, it reduces the cloud idle time to 25.5% of the total time, which verifies the advantages of our method in terms of both model performance and training overhead.