mlsanityData to 14 Oct 2026

AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator

arXiv 2506.17805 ↗First posted 21 Jun 2025cs.CR cs.LGcited by 1

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Abstract (arXiv)

Federated Learning (FL) enables collaborative learning without exposing clients' data. While clients only share model updates with the aggregator, studies reveal that aggregators can infer sensitive information from these updates. Secure Aggregation (SA) protects individual updates during transmission; however, recent work demonstrates a critical vulnerability where adversarial aggregators manipulate client selection to bypass SA protections, constituting a Biased Selection Attack (BSA). Although verifiable random selection prevents BSA, it precludes informed client selection essential for FL performance. We propose Adversarial Robust Federated Learning (AdRo-FL), which simultaneously enables: informed client selection based on client utility, and robust defense against BSA maintaining privacy-preserving aggregation. AdRo-FL implements two client selection frameworks tailored for distinct settings. The first framework assumes clients are grouped into clusters based on mutual trust, such as different branches of an organization. The second framework handles distributed clients where no trust relationships exist between them. For the cluster-oriented setting, we propose a novel defense against BSA by (1) enforcing a minimum client selection quota from each cluster, supervised by a cluster-head in every round, and (2) introducing a client utility function to prioritize efficient clients. For the distributed setting, we design a two-phase selection protocol: first, the aggregator selects the top clients based on our utility-driven ranking; then, a verifiable random function (VRF) ensures a BSA-resistant final selection. AdRo-FL also applies quantization to reduce communication overhead and sets strict transmission deadlines to improve energy efficiency. AdRo-FL achieves up to 1.85× faster time-to-accuracy and up to 1.06× higher final accuracy compared to insecure baselines.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 21 Jun 2025.

Builds on

18 of 40 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  3. 2018Amplification by Shuffling: From Local to Central Differential Privacy via Anonymitycited by 164
  4. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  5. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  6. 20158-Bit Approximations for Parallelism in Deep Learningcited by 56
  7. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  8. 2020Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycited by 22
  9. 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancercited by 17
  10. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  11. 2021Client Selection in Federated Learning based on Gradients Importancecited by 9
  12. 2022FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learningcited by 9
6 more
  1. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  2. 2022Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacycited by 6
  3. 2022Blockchain-based Secure Client Selection in Federated Learningcited by 5
  4. 2022FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devicescited by 4
  5. 2024Lotto: Secure Participant Selection against Adversarial Servers in Federated Learningcited by 2
  6. 2022Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)cited by 1

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  1. 2024FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
  2. 2026Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning
  3. 2025FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
  4. 2023Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
  5. 2026Dynamic Meta-Layer Aggregation for Byzantine-Robust Federated Learning
  6. 2026FAROS: Robust Federated Learning with Adaptive Scaling against Backdoor Attacks
  7. 2021FedRAD: Federated Robust Adaptive Distillation
  8. 2025RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients