  Real-time video analytics systems typically place models with fewer weights
on edge devices to reduce latency. The distribution of video content features
may change over time for various reasons (i.e. light and weather change) ,
leading to accuracy degradation of existing models, to solve this problem,
recent work proposes a framework that uses a remote server to continually train
and adapt the lightweight model at edge with the help of complex model.
However, existing analytics approaches leave two challenges untouched: firstly,
retraining task is compute-intensive, resulting in large model update delays;
secondly, new model may not fit well enough with the data distribution of the
current video stream. To address these challenges, in this paper, we present
EdgeSync, EdgeSync filters the samples by considering both timeliness and
inference results to make training samples more relevant to the current video
content as well as reduce the update delay, to improve the quality of training,
EdgeSync also designs a training management module that can efficiently adjusts
the model training time and training order on the runtime. By evaluating real
datasets with complex scenes, our method improves about 3.4% compared to
existing methods and about 10% compared to traditional means.
