Yang, Y, Yang, R orcid.org/0000-0001-6334-4925, Peng, H et al. (4 more authors) (2023) FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection. In: WWW '23: Proceedings of the ACM Web Conference 2023. WWW '23: The ACM Web Conference 2023, 30 Apr - 04 May 2023, Austin, TX, USA. ACM , pp. 1314-1323. ISBN 978-1-4503-9416-1
Abstract
Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture make it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client’s local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Owner/author(s) | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WWW '23: Proceedings of the ACM Web Conference 2023, https://doi.org/10.1145/3543507.3583500. |
Keywords: | social bot detection; contrastive federated learning; knowledge distillation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Mar 2023 12:17 |
Last Modified: | 13 May 2023 01:28 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3543507.3583500 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197549 |