EdgeSync: Accelerating Edge-Model Updates for Data Drift through Adaptive Continuous Learning

2025-10-18

AI Summary

In edge video analytics, data drift caused by illumination and weather variations degrades the accuracy of lightweight models. To address this, we propose a dynamic model update method that jointly optimizes timeliness and relevance. Our approach features three key contributions: (1) an adaptive sample filtering mechanism that jointly leverages inference confidence and temporal decay to prioritize informative, recent samples; (2) a dynamic training scheduler enabling edge-cloud collaborative incremental continual learning; and (3) an integrated strategy combining adaptive sampling with lightweight model distillation to balance update efficiency and generalization capability. Experiments on a real-world edge video dataset demonstrate that our method improves accuracy by 3.4% over state-of-the-art approaches and by approximately 10% over conventional periodic update baselines. It significantly enhances model robustness and temporal adaptivity in open-world deployment scenarios.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Video Understanding & Activity Analysis
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Real-time video analytics systems typically deploy lightweight models on edge devices to reduce latency. However, the distribution of data features may change over time due to various factors such as changing lighting and weather conditions, leading to decreased model accuracy. Recent frameworks try to address this issue by leveraging remote servers to continuously train and adapt lightweight edge models using more complex models in the cloud. Despite these advancements, existing methods face two key challenges: first, the retraining process is compute-intensive, causing significant delays in model updates; second, the new model may not align well with the evolving data distribution of the current video stream. To address these challenges, we introduce EdgeSync, an efficient edge-model updating approach that enhances sample filtering by incorporating timeliness and inference results, thus ensuring training samples are more relevant to the current video content while reducing update delays. Additionally, EdgeSync features a dynamic training management module that optimizes the timing and sequencing of model updates to improve their timeliness. Evaluations on diverse and complex real-world datasets demonstrate that EdgeSync improves accuracy by approximately 3.4% compared to existing methods and by about 10% compared to traditional approaches.

Problem

• Accelerating edge model updates for data drift
• Reducing compute-intensive retraining delays
• Aligning models with evolving video data distribution

Innovation

• Adaptive continuous learning for edge model updates
• Enhanced sample filtering with timeliness and inference
• Dynamic training management optimizes update timing