Kavli Affiliate: Feng Wang
| First 5 Authors: Wenbin Zhai, Feng Wang, Liang Liu, Youwei Ding, Wanying Lu
| Summary:
Existing FL-based approaches are based on the unrealistic assumption that the
data on the client-side is fully annotated with ground truths. Furthermore, it
is a great challenge how to improve the training efficiency while ensuring the
detection accuracy in the highly heterogeneous and resource-constrained IoT
networks. Meanwhile, the communication cost between clients and the server is
also a problem that can not be ignored. Therefore, in this paper, we propose a
Federated Semi-Supervised and Semi-Asynchronous (FedS3A) learning for anomaly
detection in IoT networks. First, we consider a more realistic assumption that
labeled data is only available at the server, and pseudo-labeling is utilized
to implement federated semi-supervised learning, in which a dynamic weight of
supervised learning is exploited to balance the supervised learning at the
server and unsupervised learning at clients. Then, we propose a
semi-asynchronous model update and staleness tolerant distribution scheme to
achieve a trade-off between the round efficiency and detection accuracy.
Meanwhile, the staleness of local models and the participation frequency of
clients are considered to adjust their contributions to the global model. In
addition, a group-based aggregation function is proposed to deal with the
non-IID distribution of the data. Finally, the difference transmission based on
the sparse matrix is adopted to reduce the communication cost. Extensive
experimental results show that FedS3A can achieve greater than 98% accuracy
even when the data is non-IID and is superior to the classic FL-based
algorithms in terms of both detection performance and round efficiency,
achieving a win-win situation. Meanwhile, FedS3A successfully reduces the
communication cost by higher than 50%.
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