Abstract
Federated learning is a machine learning framework that solves the problem of data silos under secure data protection measures and is gradually becoming a machine learning paradigm for future AI development. In recent years, federated learning has evolved in research areas such as security, model aggregation, and incentive mechanisms. However, the direction of interpretability of the model in the federated learning framework has not been explored. To bridge this gap, this paper proposes an interpretable model of Federated Concept Learning (FCL). FCL is trained on the client side using Bottleneck Concept Learner (BotCL) to generate human-understandable concepts. Each client uploads the co-occurrence scores of the concepts and classes obtained from the training to the server, and in order to mitigate the influence of possible malicious clients on the model, the server aggregates the obtained co-occurrence scores after optimization. The aggregated scores are then sent to the clients to update the model, which performs the classification task only by the presence or absence of the concepts. Experimental results show that our model has equivalent performance to other federated learning methods and successfully mitigates the impact of malicious client degree on the model performance, as well as provides an interpretation of the model classification results.
Code is available at https://github.com/jiaxin-shen/FCL..
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Acknowledgment
This work is supported by the High-Performance Computing Center of Dalian Maritime University, and the Fundamental Research Funds for the Central Universities, JLU.
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Shen, J., Tao, X., Li, L., Li, Z., Wang, B. (2024). Explaining Federated Learning Through Concepts in Image Classification. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14491. Springer, Singapore. https://doi.org/10.1007/978-981-97-0808-6_19
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