# Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

arXiv: https://arxiv.org/abs/2204.13256 · HTML: https://mlsanity.com/paper/2204.13256/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2022.

## Built on by (most cited first, 18 of 18)

- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting](https://mlsanity.com/paper/2302.06079/) (2023)
- [Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning](https://mlsanity.com/paper/2304.10783/) (2023)
- [FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy](https://mlsanity.com/paper/2310.09789/) (2023)
- [Influence-Oriented Personalized Federated Learning](https://mlsanity.com/paper/2410.03315/) (2024)
- [MARS: A Malignity-Aware Backdoor Defense in Federated Learning](https://mlsanity.com/paper/2509.20383/) (2025)
- [Federated Multi-Armed Bandits Under Byzantine Attacks](https://mlsanity.com/paper/2205.04134/) (2022)
- [A Systematic Literature Review on Client Selection in Federated Learning](https://mlsanity.com/paper/2306.04862/) (2023)
- [MISA: Unveiling the Vulnerabilities in Split Federated Learning](https://mlsanity.com/paper/2312.11026/) (2023)
- [Understanding Byzantine Robustness in Federated Learning with A Black-box Server](https://mlsanity.com/paper/2408.06042/) (2024)
- [Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols](https://mlsanity.com/paper/2410.22680/) (2024)
- [FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks](https://mlsanity.com/paper/2508.18737/) (2025)
- [A Unified Momentum-based Paradigm of Decentralized SGD for Non-Convex Models and Heterogeneous Data](https://mlsanity.com/paper/2303.00179/) (2023)
- [Fed-Credit: Robust Federated Learning with Credibility Management](https://mlsanity.com/paper/2405.11758/) (2024)
- [SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks](https://mlsanity.com/paper/2409.15067/) (2024)
- [FRIDA: Free-Rider Detection using Privacy Attacks](https://mlsanity.com/paper/2410.05020/) (2024)
- [EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning](https://mlsanity.com/paper/2506.13612/) (2025)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have several flaws: the server irrationally chooses malicious clients for aggregation even after they have been detected in previous rounds; the defenses perform ineffectively against sybil attacks or in the heterogeneous data setting. To overcome these issues, we propose MAB-RFL, a new method for robust aggregation in FL. By modelling the client selection as an extended multi-armed bandit (MAB) problem, we propose an adaptive client selection strategy to choose honest clients that are more likely to contribute high-quality updates. We then propose two approaches to identify malicious updates from sybil and non-sybil attacks, based on which rewards for each client selection decision can be accurately evaluated to discourage malicious behaviors. MAB-RFL achieves a satisfying balance between exploration and exploitation on the potential benign clients. Extensive experimental results show that MAB-RFL outperforms existing defenses in three attack scenarios under different percentages of attackers.