FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916875284250624 |
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| author | Jiang, Haocheng Shen, Hua Zhang, Jixin Susilo, Willy Zhang, Mingwu |
| author_facet | Jiang, Haocheng Shen, Hua Zhang, Jixin Susilo, Willy Zhang, Mingwu |
| contents | Federated learning is a distributed training framework vulnerable to Byzantine attacks, particularly when over 50% of clients are malicious or when datasets are highly non-independent and identically distributed (non-IID). Additionally, most existing defense mechanisms are designed for specific attack types (e.g., gradient similarity-based schemes can only defend against outlier model poisoning), limiting their effectiveness. In response, we propose FedGuard, a novel federated learning mechanism. FedGuard cleverly addresses the aforementioned issues by leveraging the high sensitivity of membership inference to model bias. By requiring clients to include an additional mini-batch of server-specified data in their training, FedGuard can identify and exclude poisoned models, as their confidence in the mini-batch will drop significantly. Our comprehensive evaluation unequivocally shows that, under three highly non-IID datasets, with 90% of clients being Byzantine and seven different types of Byzantine attacks occurring in each round, FedGuard significantly outperforms existing robust federated learning schemes in mitigating various types of Byzantine attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients Jiang, Haocheng Shen, Hua Zhang, Jixin Susilo, Willy Zhang, Mingwu Cryptography and Security Distributed, Parallel, and Cluster Computing Federated learning is a distributed training framework vulnerable to Byzantine attacks, particularly when over 50% of clients are malicious or when datasets are highly non-independent and identically distributed (non-IID). Additionally, most existing defense mechanisms are designed for specific attack types (e.g., gradient similarity-based schemes can only defend against outlier model poisoning), limiting their effectiveness. In response, we propose FedGuard, a novel federated learning mechanism. FedGuard cleverly addresses the aforementioned issues by leveraging the high sensitivity of membership inference to model bias. By requiring clients to include an additional mini-batch of server-specified data in their training, FedGuard can identify and exclude poisoned models, as their confidence in the mini-batch will drop significantly. Our comprehensive evaluation unequivocally shows that, under three highly non-IID datasets, with 90% of clients being Byzantine and seven different types of Byzantine attacks occurring in each round, FedGuard significantly outperforms existing robust federated learning schemes in mitigating various types of Byzantine attacks. |
| title | FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.00636 |