Federated model with contrastive learning and adaptive control variates for human activity recognition

Ignatius IWAN; Bernardo Nugroho YAHYA; Seok-Lyong LEE

doi:10.1631/FITEE.2400797

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Federated model with contrastive learning and adaptive control variates for human activity recognition

Regular Papers | Updated：2025-07-02

- Federated model with contrastive learning and adaptive control variates for human activity recognition
- 用于人体活动识别的基于对比学习与自适应变量控制的联邦模型
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 6, Pages: 896-911(2025)
- Affiliations：
  
  Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea
- Author bio：
  
  E-mail: ignatiusiwan@hufs.ac.kr
  ‡ Corresponding author
  sllee@hufs.ac.kr
- Funds：
- DOI：10.1631/FITEE.2400797
  CLC： TP391
- Received：13 September 2024，
  
  Revised：08 January 2025，
  
  Published：2025-06
- Accepted：
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Ignatius IWAN, Bernardo Nugroho YAHYA, Seok-Lyong LEE. Federated model with contrastive learning and adaptive control variates for human activity recognition[J]. Frontiers of information technology & electronic engineering, 2025, 26(6): 896-911.
DOI：

Ignatius IWAN, Bernardo Nugroho YAHYA, Seok-Lyong LEE. Federated model with contrastive learning and adaptive control variates for human activity recognition[J]. Frontiers of information technology & electronic engineering, 2025, 26(6): 896-911. DOI： 10.1631/FITEE.2400797.

摘要

随着隐私问题日益凸显，目前亟需一种通信安全的方法，用于在用户活动数据上训练人体活动识别模型。联邦学习作为一种备受关注的技术，可以在保护数据隐私的同时促进服务器与客户端之间的模型训练。然而，传统联邦学习方法通常假设各客户端数据是相互独立且同分布的，这在实际场景中却并不成立。现实场景中的人类活动具有差异性，导致相同行为在不同客户端执行时会产生系统性偏差。这导致了本地模型目标偏离全局模型目标，进而阻碍整体收敛。为此，本文基于对比学习及自适应变量控制，提出一种名为FedCoad的联邦模型来处理人体活动识别中的客户端偏差。模型对比学习将全局模型和本地模型之间的表征差距最小化，有助于全局模型的收敛。在本地模型更新期间，自适应控制变量会根据模型权重和控制变量更新的变化速率对本地模型更新进行惩罚。我们的实验结果表明，FedCoad在人体活动识别基准数据集上的表现优于现有最先进的联邦学习算法。

Abstract

Recent attention to privacy issues demands a communication-safe method for training human activity recognition (HAR) models on client activity data. Federated learning (FL) has become a compelling technique to facilitate model training between the server and clients while preserving data privacy. However

classical FL methods often assume independent and identically distributed (IID) data among clients. This assumption does not hold true in practical scenarios. Human activity in real-world scenarios varies

resulting in skewness where identical activities are executed uniquely across clients. This leads to local model objectives drifting away from the global model objective

thereby impeding overall convergence. To address this challenge

we propose FedCoad

a novel federated model leveraging contrastive learning with adaptive control variates to handle the skewness among HAR clients. Model contrastive learning minimizes the gap in representation between global and local models to help global model convergence. During local model updates

the adaptive control variates penalize the local model updates with respect to the model weight and the rate of change from the control variates update. Our experiments show that FedCoad outperforms state-of-the-art FL algorithms on HAR benchmark datasets.

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references

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