可靠性感知的分层联邦学习机制

刘晓燕; 余振; 梁晶语; 林伯韬; 黄霁崴

doi:10.12263/DZXB.20250852

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可靠性感知的分层联邦学习机制

学术论文 | 更新时间：2026-06-22

- 可靠性感知的分层联邦学习机制
- A Reliability-Aware Mechanism for Hierarchical Federated Learning
- 电子学报 2026年54卷第1期页码：262-275
- 作者机构：
  
  1.中国石油大学（北京）石油数据挖掘北京市重点实验室，北京 102249
  2.中石油（北京）数智研究院有限公司，北京 102206
- 作者简介：
  
  刘晓燕女，1996年11月出生于山东省德州市。现为中国石油大学（北京）人工智能学院先进科学与工程计算专业博士研究生。主要研究方向为边缘智能、联邦学习。E-mail: liuxy@student.cup.edu.cn
  余振男，2004年2月生于河南省信阳市。现为中国石油大学（北京）人工智能学院人工智能专业本科生。主要研究方向为联邦学习、边缘智能。E-mail: 2022012251@student.cup.edu.cn
  梁晶语女，1998年2月出生于安徽省宿州市。现为中石油（北京）数智研究院有限公司中级工程师。主要研究方向为边缘计算、工业软件。E-mail: liangjingyu@petrochina.com.cn
  林伯韬男，1983年11月出生于福建省武平县。现为中国石油大学（北京）教授、博士生导师、人工智能学院院长。主要研究方向为智能石油工程、工业数字孪生和油气领域大模型。E-mail: linbotao@cup.edu.cn
  黄霁崴男，1987年2月出生于北京市。2014年毕业于清华大学计算机科学与技术系，现为中国石油大学（北京）教授、博士生导师、人工智能学院副院长、石油数据挖掘北京市重点实验室主任。入选北京市科技新星、北京市优秀人才等。主要研究方向为物联网、边缘计算、服务计算。中国电子学会会员编号：E190158513M。E-mail: huangjw@cup.edu.cn
- 基金信息：
  
  国家自然科学基金(62572484);中国石油大学（北京）学科前沿交叉探索专项资助(2462024XKQY003)
- DOI：10.12263/DZXB.20250852
  中图分类号： TP301;TN92
- 收稿：2025-09-27，
  
  录用：2026-01-21，
  
  纸质出版：2026-01-25
- 稿件说明：
移动端阅览
刘晓燕, 余振, 梁晶语, 等. 可靠性感知的分层联邦学习机制[J]. 电子学报, 2026, 54(01): 262-275.

LIU Xiaoyan, YU Zhen, LIANG Jingyu, et al. A Reliability-Aware Mechanism for Hierarchical Federated Learning[J]. Acta Electronica Sinica, 2026, 54(01): 262-275.
刘晓燕, 余振, 梁晶语, 等. 可靠性感知的分层联邦学习机制[J]. 电子学报, 2026, 54(01): 262-275. DOI：10.12263/DZXB.20250852

LIU Xiaoyan, YU Zhen, LIANG Jingyu, et al. A Reliability-Aware Mechanism for Hierarchical Federated Learning[J]. Acta Electronica Sinica, 2026, 54(01): 262-275. DOI：10.12263/DZXB.20250852

摘要

分层联邦学习（Hierarchical Federated Learning，HFL）通过“终端-边缘-云”的层次化组织，在边缘侧执行组内聚合、云侧进行全局聚合，以实现跨区域的高效协同训练。然而，客户端数据普遍呈非独立同分布（Non-Independent and Identically Distributed，Non-IID）特性，易导致组内更新方向不一致、梯度偏移乃至收敛震荡，进而削弱全局模型性能。同时，边缘服务器受资源约束、负载波动与链路不稳定影响，存在性能退化甚至失效风险，可能引发组内聚合中断，降低系统稳定性与任务完成效率。对此，本文提出一种可靠性感知的分层联邦学习框架（Reliability-aware Hierarchical Federated Learning，R-HFL），将训练过程划分为可靠性感知分组阶段和全局聚合阶段。在分组阶段，综合客户端模型语义特征与地理邻近性进行联合聚类，以提升组内统计一致性并缓解Non-IID诱发的梯度偏移，同时引入边缘节点可靠性指标作为约束进行协同选择，优先选取高可靠性边缘服务器作为组内中间聚合器，从而降低聚合服务中断风险。进一步地，考虑边缘服务器可靠性的时变性与联邦训练的长期性，本文设计了失效触发的可靠性感知服务迁移机制。当组内聚合器发生故障时，将聚合任务动态迁移至可用边缘服务器，以保障训练连续性。为实现迁移过程的自适应决策，本文将多客户端迁移建模为马尔可夫决策过程（Markov Decision Process，MDP），采用多智能体近端策略优化（Multi-Agent Proximal Policy Optimization，MAPPO）于集中式训练、分布式执行（Centralized Training with Decentralized Execution，CTDE）框架中学习迁移策略；通过统一的奖励与约束机制动态权衡迁移成本、迁移后通信开销与语义分布相似度，从而实现迁移目标的自适应选择、迁移后快速适配与收敛稳定性维持。最后，在两个真实数据集及多种Non-IID划分场景下进行实验验证。结果表明，所提R-HFL在全局模型精度与收敛速度上优于基线方法，并能在边缘服务器失效情况下显著降低训练中断风险与迁移开销，提升系统整体鲁棒性和故障容忍能力。

Abstract

Hierarchical federated learning (HFL) operates in a client-edge-cloud architecture

where intra-group aggregation is carried out at the edge and global aggregation is performed in the cloud

enabling efficient distributed collaborative training. However

client data is typically non-independent and identically distributed (Non-IID)

which may yield inconsistent local updates

leading to gradient drift and convergence instability

and degrading global model performance. Meanwhile

edge servers are subject to resource limitations

workload fluctuations

and unstable links

which can cause performance degradation or even failures. Such events may interrupt intra-group aggregation

undermining system stability and task completion efficiency. To address these challenges

this paper proposes a reliability-aware hierarchical federated learning framework (R-HFL) that decomposes the training procedure into a reliability-aware grouping stage and a global aggregation stage. In the grouping stage

we jointly cluster clients by integrating model semantic similarity and geographic proximity

improving intra-group statistical consistency and mitigating gradient drift induced by Non-IID data. In addition

an edge reliability metric is incorporated as a reliability-aware selection criterion

prioritizing highly reliable edge servers as group-level aggregators to reduce the risk of aggregation interruption. Furthermore

to account for the time-varying reliability of edge servers and the long-term horizon of federated training

we design a failure-triggered task migration mechanism: when a group-level aggregator fails

the aggregation task is dynamically migrated to an available edge server to maintain training continuity. To enable adaptive migration decisions

we formulate the migration process as a markov decision process (MDP) and adopt multi-agent proximal policy optimization (MAPPO) under centralized training and decentralized execution (CTDE) to learn migration policies. A unified reward function with constraints is further designed to dynamically balance migration cost

post-migration communication overhead

and semantic distribution similarity

facilitating an adaptive trade-off among objectives

fast migration adaptation

and sustained convergence stability. Finally

extensive experiments are conducted on two real-world datasets under different Non-IID scenarios. The results demonstrate that R-HFL consistently outperforms baseline methods in terms of global accuracy and convergence rate

while substantially reducing the risk of training disruption and migration overhead under edge server failures

thereby improving overall system robustness and fault tolerance.

关键词

Keywords

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