Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering
Learn to defend Federated Learning against adversarial attacks using loss-based client clustering, a crucial technique for robust collaborative model training
- Implement Federated Learning with a trusted server and clients
- Identify and cluster clients based on their loss values to detect potential adversarial attacks
- Use the clustered clients to update the global model, ignoring or down-weighting updates from suspicious clients
- Test the robustness of the model against various adversarial attack scenarios
- Apply loss-based client clustering to real-world federated learning applications, such as collaborative model training across multiple organizations
Data scientists and machine learning engineers working on federated learning projects will benefit from this technique to ensure the security and reliability of their models, especially in scenarios where clients may be subject to adversarial attacks
💡 Loss-based client clustering can effectively detect and mitigate adversarial attacks in Federated Learning scenarios, ensuring the reliability and security of collaborative model training
🚀 Defend your Federated Learning models against adversarial attacks with loss-based client clustering! 🤖
Key Takeaways
Learn to defend Federated Learning against adversarial attacks using loss-based client clustering, a crucial technique for robust collaborative model training
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Abstract:
arXiv:2508.12672v4 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wherein FL clients are subject to adversarial (Byzantine) attacks, while the FL server is trusted (honest) and has a trustworthy side dataset. This may correspond to, e.g., cases where the server possesses trusted data prior to federation, or to the presence of a trusted client that temporarily assume
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