  In federated learning (FL), it is commonly assumed that all data are placed
at clients in the beginning of machine learning (ML) optimization (i.e.,
offline learning). However, in many real-world applications, it is expected to
proceed in an online fashion. To this end, online FL (OFL) has been introduced,
which aims at learning a sequence of global models from decentralized streaming
data such that the so-called cumulative regret is minimized. Combining online
gradient descent and model averaging, in this framework, FedOGD is constructed
as the counterpart of FedSGD in FL. While it can enjoy an optimal sublinear
regret, FedOGD suffers from heavy communication costs. In this paper, we
present a communication-efficient method (named OFedIQ) by means of
intermittent transmission (enabled by client subsampling and periodic
transmission) and quantization. For the first time, we derive the regret bound
that captures the impact of data-heterogeneity and the communication-efficient
techniques. Through this, we efficiently optimize the parameters of OFedIQ such
as sampling rate, transmission period, and quantization levels. Also, it is
proved that the optimized OFedIQ can asymptotically achieve the performance of
FedOGD while reducing the communication costs by 99%. Via experiments with real
datasets, we demonstrate the effectiveness of the optimized OFedIQ.
