Abstract
Breaking down data silos and promoting data circulation and cooperation is an important topic in the digital age. As data security and privacy protection have received widespread attention, the traditional cooperation model based on data centralization has been challenged. Federated learning provides technical solutions to solve this problem, but the characteristics of multi-party cooperation and data invisibility make it face the risk of data fraud. Malicious participants can manipulate data individually or in collusion to illegally obtain data or influence federated learning model. This paper proposes a novel data fraud detection method based on distributed collaborative representation and realizes the effective detection of federated learning data fraud through collaborative clustering, adaptive representation and dynamic weighting. The method proposed in this paper overcomes weakness in the existing methods that detect data fraud mechanically and statically, which cannot be organically combined with the training objectives and process. It realizes the dynamically continuous anti-collusion soft constraint detection while ensuring fraud detection and contribution evaluation are relatively independent. Our research is of great significance for federated learning to deal with the risk of data fraud and better apply to real-world scenarios.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (grant numbers 72271059, 71971067). Chenghong Zhang is the corresponding author.
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Zhang, Z., Zhang, C., Chen, G., Xiao, S., Huang, L. (2023). Distinguishing Good from Bad: Distributed-Collaborative-Representation-Based Data Fraud Detection in Federated Learning. In: Nah, F., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2023. Lecture Notes in Computer Science, vol 14039. Springer, Cham. https://doi.org/10.1007/978-3-031-36049-7_19
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