EdgeSync: Faster Edge-model Updating via Adaptive Continuous Learning for Video Data Drift

TL;DR

EdgeSync is a novel adaptive learning framework that enhances real-time video analytics on edge devices by efficiently updating models to handle data drift, reducing delay and improving accuracy in dynamic environments.

Contribution

The paper introduces EdgeSync, a method that filters training samples and manages training dynamically to improve edge model updates amidst video data drift.

Findings

Improves accuracy by 3.4% over existing methods.

Reduces model update delay through sample filtering.

Enhances adaptation to changing video content.

Abstract

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.