Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (5): 388-403.doi: 10.11896/jsjkx.250300131

• Computer Architecture • Previous Articles     Next Articles

Novel Multi-task Federated Learning Based Approach for Detecting and Diagnosing Anomalies inCloud Microservices

CHEN Peng1, HAO Junfeng1, XIA Yunni2, LI Xi1   

  1. 1 School of Computer and Software Engineering, Xihua University, Chengdu 610039, China
    2 College of Computer, Chongqing University, Chongqing 400044, China
  • Received:2025-03-24 Revised:2025-06-24 Published:2026-05-08
  • About author:CHEN Peng,born in 1979,Ph.D,professor,is a executive member of CCF(No.B3144M).His main research interests include cloud computing,service computing and anomaly detection.
    XIA Yunni,born in 1980,Ph.D,professor,doctoral supervisor,is a member of CCF(No.23641M).His main research interests include cloud computing,ser-vice computing and edge computing.
  • Supported by:
    National Natural Science Foundation of China(62172062),Sichuan Provincial Natural Science Foundation(2024NSFTD0008) and Science and Technology Program of Sichuan Province(2020JDRC0067,2023JDRC0087).

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Abstract: Microservice architecture is widely used for application development in cloud environments,and its essence is to build applications through a series of functionally independent small autonomous services with high cohesion,high availability,low coupling,and good scalability.However,since microservice architecture is a distributed computing architecture with high dynamics and real-time system anomaly detection of distributed and independent microservices is a very challenging task,determining the category of detected anomalies is even more critical in practical applications.To solve the above problems,a multi-task federated learning-based system anomaly detection and diagnosis(MT-FL-SADD) method is proposed.Firstly,a multi-task federated lear-ning(MT-FL) distributed learning framework is proposed,which is used to construct an anomaly detection and diagnosis model for each microservice.Secondly,in order to identify the complex system anomaly patterns and features at the runtime of microservices,a feature extractor based on squeeze excitetion and external attention(SE-EA-EDN) is constructed to efficiently extract the features of real-time data from microservices monitoring at the runtime.Finally,a local-global feature-based parallel knowledge transfer(LGF-PKT) framework is designed to parallelize the weight update of local and global features.To validate the effectiveness of the proposed method,MT-FL-SADD improves the average Macro F1 by 33.9% and the average Micro F1 by 33.4% compared to other federated learning methods on the microservices benchmarking platforms Sock Shop and Train Ticket,and also improves the average F1 by 2.2% compared to other federated learning methods on SWaT,SMD and SKAB.

Key words: Microservice architecture, Multi-task federated learning, Distributed computing architectures, System anomaly detection and diagnosis

CLC Number: 

  • TP181
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