mlsanityData to 14 Oct 2026

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

arXiv 2607.06612 ↗First posted 7 Jul 2026cs.CR cs.AIcited by 0

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Abstract (arXiv)

Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks. Although recent work has explored secure and Byzantine-resilient FL protocols, they face a fundamental trade-off among privacy, integrity, and verifiability, and incur substantial computational and communication overhead due to the heavy use of cryptographic primitives. In this work, we propose PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation. PRoVeFL employs multiple servers leveraging multi-key fully homomorphic encryption. Each client encrypts its local model updates and distributes encrypted shares to all servers. This design enables a hybrid computation model in which ciphertext operations are carefully offloaded to the plaintext domain under strict privacy constraints to efficiently evaluate complex statistical aggregation rules. PRoVeFL is compatible with a wide range of state-of-the-art Byzantine-robust aggregation algorithms (e.g., Krum, Trimmed Mean, FLTrust, norm clipping, MESAS, and more) and further enhances them with verifiability mechanisms that require minimal trust in at least one honest server. We evaluate it across different settings and demonstrate its scalability with varying numbers of parameters and participants. PRoVeFL improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 60 references that are in the map. First posted on arXiv 7 Jul 2026.

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21 of 60 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018LEAF: A Benchmark for Federated Settingscited by 792
  4. 2018How To Backdoor Federated Learningcited by 765
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  6. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  8. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  9. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  10. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  11. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 48
  12. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
9 more
  1. 2017Prio: Private, Robust, and Scalable Computation of Aggregate Statisticscited by 36
  2. 2021Source Inference Attacks in Federated Learningcited by 34
  3. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 27
  4. 2024Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learningcited by 6
  5. 2022MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clusteringcited by 5
  6. 2025TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learningcited by 4
  7. 2017Order-Preserving Encryption Using Approximate Integer Common Divisorscited by 1
  8. 2023Robust and Actively Secure Serverless Collaborative Learningcited by 1
  9. 2025Towards Collaborative Anti-Money Laundering Among Financial Institutionscited by 1

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