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Mobile crowdsensing has gained significant attention in recent years and hasbecome a critical paradigm for emerging Internet of Things applications. Thesensing devices continuously generate a significant quantity of data, whichprovide tremendous opportunities to develop innovative intelligentapplications. To utilize these data to train machine learning models while notcompromising user privacy, federated learning has become a promising solution.However, there is little understanding of whether federated learning algorithmsare guaranteed to converge. We reconsider model averaging in federated learningand formulate it as a gradient-based method with biased gradients. This novelperspective assists analysis of its convergence rate and provides a newdirection for more acceleration. We prove for the first time that the federatedaveraging algorithm is guaranteed to converge for non-convex problems, withoutimposing additional assumptions. We further propose a novel acceleratedfederated learning algorithm and provide a convergence guarantee. Simulatedfederated learning experiments are conducted to train deep neural networks onbenchmark datasets, and experimental results show that our proposed methodconverges faster than previous approaches.

Faster On-Device Training Using New Federated Momentum Algorithm