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A PyTorch implementation of "Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective" (AAAI 2025)

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Spattack

Source code for AAAI 2025 paper "Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective" Paper link: https://arxiv.org/pdf/2501.03301

QQ_1736335827472

🔬 Experiment

Spattack-O-D

python main.py --attack Spattack_O --clients_limit 0.052631 --defense Mean

Spattack-O-S

python main.py --attack Spattack_O --sample_items --clients_limit 0.052631 --defense Mean

Spattack-L-D

python main.py --attack Spattack_L --clients_limit 0.052631 --defense Mean

Spattack-L-S

python main.py --attack Spattack_L --sample_items --clients_limit 0.052631 --defense Mean

You can specific the "--defense" and "--dataset" to change the defense and dataset, respectively.

📝 Citation and Reference

If you find this paper useful, please consider staring 🌟 this repo and citing 📑 our paper:


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A PyTorch implementation of "Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective" (AAAI 2025)

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