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FedCare: towards interactive diagnosis of federated learning systems

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Abstract

Federated Learning (FL) is a machine learning paradigm where multiple data owners collaboratively train a model under the coordination of a central server, while keeping all data decentralized. Such a paradigm allows models to be trained effectively while avoiding data privacy leakage. However, federated learning is vulnerable to various kinds of failures as a result of both intentional (malicious) and none intentional (non-malicious) attacks. Existing studies on attacks in federated learning are mostly dedicated to the automatic defense against malicious attacks (e.g., data poisoning attacks). Relatively, less attention has been given to handling non-malicious failures (e.g., non-independent and identically distributed data failures), which are actually more common and difficult-to-handle in a federated learning setting. In this paper, we propose FedCare, a real-time visual diagnosis approach for handling failures in federated learning systems. The functionality of FedCare includes the identification of failures, the assessment of their nature (malicious or non-malicious), the study of their impact, and the recommendation of adequate defense strategies. Our design is multi-faceted, giving perspectives from the angles of model performance, anomaly/contribution assessment of clients, features maps, group activities, and client impact. We demonstrate the effectiveness of our approach through two case studies, a quantitative experiment and an expert interview.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62132017, 61772456, 61761136020), the Zhejiang Provincial Natural Science Foundation of China (LD24F020011), and the “Pioneer and Leading Goose” R&D Program of Zhejiang (2024C01167).

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Authors and Affiliations

  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, China

    Tianye Zhang, Haozhe Feng, Zexian Chen & Wei Chen

  2. Digital Grid Research Institute, China Southern Power Grid, Guangzhou, 510663, China

    Wenqi Huang, Lingyu Liang & Huanming Zhang

  3. Zhejiang University - China Southern Power Grid Joint Research Centre on AI, Hangzhou, 310058, China

    Wenqi Huang, Lingyu Liang, Huanming Zhang & Wei Chen

  4. Department of Computer Science, National University of Singapore, Singapore, 117597, Singapore

    Anthony K. H. Tung

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  1. Tianye Zhang
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Correspondence to Wei Chen.

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Competing interests Wei CHEN is an Editorial Board member of the journal and a co-author of this article. To minimize bias, they were excluded from all editorial decision-making related to the acceptance of this article for publication. The remaining authors declare no conflict of interest.

Additional information

Tianye Zhang received her BS in mathematics from Zhejiang University, China in 2016. She is currently a PhD student in the College of Computer Science and Technology at the Zhejiang University, China. Her research interests include data mining and visual analytics.

Haozhe Feng received his BS in mathematics statistics from Zhejiang University, China in 2018. He is currently a PhD student in the College of Computer Science and Technology at the Zhejiang University, China. His research interests include representation learning and distributed machine learning.

Wenqi Huang is the leader of Artificial Intelligence and Intelligent Software Team in R&D Center, Digital Grid Research Institute, China Southern Power Grid. She holds BS (2010) and PhD (2015) degrees from the Department of Information Science and Electronic Engineering, Zhejiang University, China. Her research interests span artificial intelligence, data mining and blockchain application technology in the field of power industry.

Lingyu Liang is the senior technical expert of Artificial Intelligence and Intelligent Software Team in R&D Center, Digital Grid Research Institute, China Southern Power Grid. He holds BS (2010) and MS (2014) from School of Information Scicnce and Technology, Tsinghua University, China. His research interests span artificial intelligence, data mining, and intelligent decision-making in the field of power industry.

Huanming Zhang is the researcher of Artificial Intelligence and Intelligent Software Team in R&D Center, Digital Grid Research Institute, China Southern Power Grid. He holds BS (2019) from School of Electrical Engineering, Beijing Jiaotong University and MS (2022) from School of Electrical Engineering, South China University of Technology, China. His research interests span artificial intelligence, data mining, intelligent decision-making, and large language model in the field of power industry.

Zexian Chen received his BS in computer science and technology from the Zhejiang University, China in 2018. He is currently a master student in the College of Computer Science and Technology at the Zhejiang University, China. His research interests include information visualization and visual analytics.

Anthony K. H. Tung is a professor in the Department of Computer Science, National University of Singapore, Singapore(NUS) and a junior faculty member in the NUS Graduate School for Integrative Sciences and Engineering and a SINGA supervisor. His research interests include various aspects of databases and data mining (KDD) including buffer management, frequent pattern discovery, spatial clustering, outlier detection, and classification analysis.

Wei Chen is a professor at the State Key Lab of CAD & CG, Zhejiang University, China. His research interests are visualization and visual analysis, and has published more than 30 IEEE/ACM Transactions and IEEE VIS papers. He actively served as guest or associate editors of IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Intelligent Transportation Systems, and Journal of Visualization.

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Zhang, T., Feng, H., Huang, W. et al. FedCare: towards interactive diagnosis of federated learning systems. Front. Comput. Sci. 19, 197335 (2025). https://doi.org/10.1007/s11704-024-3735-7

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