Journal of University of Chinese Academy of Sciences >
FLShadow：Byzantine-robust federated aggregation based on a trusted shadow model
Received date: 2023-10-08
Revised date: 2025-05-17
Online published: 2024-06-11
In federated learning， Byzantine nodes can carefully manipulate the model updates of clients. As a result， the central model accuracy decreases or fails to converge after aggregation. The number of communication rounds also increases. Without trusted reference gradients， the central model can not be properly aggregated solely from updates provided by untrustworthy clients. In this paper， we introduce a trusted reference and we regard it as a shadow model. To overcome this challenge， we propose a novel Byzantine robust federated aggregation method. The central server collects the shadow dataset in advance and trains a model called the shadow model. The central server compares the update direction between the client model and the trusted shadow model. Accordingly， the central server computes malicious score and marks the malicious clients. Then， the central model deletes or prunes the updates from the malicious clients. Finally， the central model aggregates the corrected gradients to ensure good convergence and maintain accuracy. The proposed method has been evaluated on a variety of model architectures and real datasets. The results show that the proposed method can effectively defend against six different Byzantine node attacks on three datasets.
Chenchen XU , Xutong WANG , Taochun WANG , Fulong CHEN , Qixu LIU . FLShadow：Byzantine-robust federated aggregation based on a trusted shadow model[J]. Journal of University of Chinese Academy of Sciences, 2026 , 43(4) : 553 -565 . DOI: 10.7523/j.ucas.2024.051
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