End-to-End Verifiable and Robust Federated Learning

• URL: http://arxiv.org/abs/2609.15521v1
• Date: Mon, 14 Sep 2026 13:13:25 GMT
• Title: End-to-End Verifiable and Robust Federated Learning
• Abstract summary: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator.<n>Two requirements are particularly important: robustness to poisoned or Byzantine client updates, and verifiability of the aggregator.<n>We present a verifiable federated learning protocol that makes a robust aggregation pipeline publicly auditable.
• Score: 0.9134244356393665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integrity risks once participants or infrastructure are not fully trustworthy. Two requirements are particularly important: robustness to poisoned or Byzantine client updates, and verifiability of the aggregator so that clients or third parties can audit the reported aggregation without learning individual updates. Existing work has largely treated these goals separately, and efficient public verifiability for robust, outlier-excluding aggregation remains limited. We present a verifiable federated learning protocol that makes a robust aggregation pipeline publicly auditable. Our design combines cryptographic commitments with non-interactive zero-knowledge proofs to certify both (i) cosine-similarity-based outlier exclusion and (ii) aggregation over the selected set, without revealing individual client updates to verifiers. In experiments under representative poisoning attacks, our method maintains high accuracy, with an average accuracy loss below 4\% across the evaluated configurations, while keeping verification overhead practical: proof artifacts can be generated and verified within minutes at the scale studied. In summary, our results show that robust outlier exclusion and public verifiability can be jointly achieved in a federated learning setting.

Related papers

• PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception [75.70273182182397]
  PerceptionRubrics is a rubric-based evaluation framework.<n>It addresses the gap between saturated benchmark scores and real-world brittleness.<n>It pairs 1,038 information-dense images with over 10,000 instance-specific rubrics.
  arXiv  Detail & Related papers  (2026-06-26T17:59:15Z)
• FERA: Uncertainty-Aware Federated Reasoning for Large Language Models [60.52562148874846]
  We study federated reasoning, where a server improves multi-step reasoning by coordinating with heterogeneous clients holding private demonstrations.<n>The key challenge is that client reliability is query-dependent, while the server cannot inspect client data to determine which contributions are trustworthy.<n>We propose Uncertainty-Aware Federated Reasoning (FERA), a training-free framework based on iterative server-client co-refinement.
  arXiv  Detail & Related papers  (2026-05-11T07:04:51Z)
• Private and Robust Contribution Evaluation in Federated Learning [44.1965210509968]
  Cross-silo federated learning allows multiple organizations to collaboratively train machine learning models without sharing raw data.<n>Secure aggregation protects privacy by hiding individual updates, yet it complicates contribution evaluation.<n>We introduce two marginal-difference contribution scores compatible with secure aggregation.
  arXiv  Detail & Related papers  (2026-02-25T09:27:40Z)
• Verifiability and Privacy in Federated Learning through Context-Hiding Multi-Key Homomorphic Authenticators [3.8529853362793194]
  Federated learning has rapidly expanded from its original inception to now have a large body of research, several frameworks, and sold in a variety of commercial offerings.<n>There are many algorithms that provide robustness in the case of malicious clients.<n>We introduce a verifiable federated learning protocol that enables clients to verify the correctness of the aggregators without compromising the confidentiality of their updates.
  arXiv  Detail & Related papers  (2025-09-05T14:57:18Z)
• Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning [57.35282510032077]
  Federated learning with secure aggregation enables private and collaborative learning from decentralised data without leaking sensitive client information.<n>QI and FedGT were proposed for contribution evaluation (CE) and misbehaviour detection (MD), respectively.<n>We combine the strengths of QI and FedGT to achieve both robust MD and accurate CE.
  arXiv  Detail & Related papers  (2025-06-30T07:40:18Z)
• Chain-of-Trust: A Progressive Trust Evaluation Framework Enabled by Generative AI [20.02079841777494]
  Chain-of-trust framework is proposed to make better use of device attribute data.<n>The framework divides the trust evaluation process into chained stages based on task decomposition.<n>generative AI is employed to analyze and interpret the collected data to produce correct evaluation results.
  arXiv  Detail & Related papers  (2025-06-20T16:33:03Z)
• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting.<n>Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines.<n> Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence [13.612214163974459]
  We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data. VALID protocol is the first to achieve a validated learning guarantee. Remarkably, VALID retains optimal performance metrics in adversary-free environments.
  arXiv  Detail & Related papers  (2024-05-12T15:55:43Z)
• Enabling Privacy-preserving Model Evaluation in Federated Learning via Fully Homomorphic Encryption [1.9662978733004604]
  Federated learning has become increasingly widespread due to its ability to train models collaboratively without centralizing sensitive data.<n>The evaluation phase presents significant privacy risks that have not been adequately addressed in the literature.<n>We propose a novel evaluation method that leverages fully homomorphic encryption.
  arXiv  Detail & Related papers  (2024-03-21T14:36:55Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents [52.55119217982361]
  We propose a joint extraction approach to handle noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective. A confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels.
  arXiv  Detail & Related papers  (2020-04-21T12:03:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.