Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
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Abstract (arXiv)
Federated learning (FL) facilitates distributed training across different IoT and edge devices, safeguarding the privacy of their data. The inherent distributed structure of FL introduces vulnerabilities, especially from adversarial devices aiming to skew local updates to their advantage. Despite the plethora of research focusing on Byzantine-resilient FL, the academic community has yet to establish a comprehensive benchmark suite, pivotal for impartial assessment and comparison of different techniques. This paper presents Blades, a scalable, extensible, and easily configurable benchmark suite that supports researchers and developers in efficiently implementing and validating novel strategies against baseline algorithms in Byzantine-resilient FL. Blades contains built-in implementations of representative attack and defense strategies and offers a user-friendly interface that seamlessly integrates new ideas. Using Blades, we re-evaluate representative attacks and defenses on wide-ranging experimental configurations (approximately 1,500 trials in total). Through our extensive experiments, we gained new insights into FL robustness and highlighted previously overlooked limitations due to the absence of thorough evaluations and comparisons of baselines under various attack settings.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2022.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 2.
7 papers.
- 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
- 2024Synergizing Foundation Models and Federated Learning: A Surveycited by 14
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
- 2024PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuningcited by 5
- 2023Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detectioncited by 3
- 2025ByzFL: Research Framework for Robust Federated Learningcited by 3
- 2024Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defensecited by 1
- 2025ByzFL: Research Framework for Robust Federated Learning30 May 2025
- 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey13 Mar 2025
- 2024PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning28 Nov 2024
- 2024Synergizing Foundation Models and Federated Learning: A Survey18 Jun 2024
- 2024Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense29 May 2024
- 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs8 Jun 2023
- 2023Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection29 Mar 2023