mlsanityData to 13 Oct 2026

RobustFed: A Truth Inference Approach for Robust Federated Learning

arXiv 2107.08402 ↗First posted 18 Jul 2021cs.LG cs.AI cs.DCcited by 9

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Abstract (arXiv)

Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestration while keeping local training datasets' privacy. However, the aggregation step in federated learning is vulnerable to adversarial attacks as the central server cannot manage clients' behavior. Therefore, the global model's performance and convergence of the training process will be affected under such attacks.To mitigate this vulnerability issue, we propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing via incorporating the worker's reliability into aggregation. We evaluate our solution on three real-world datasets with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning and is resilient to various types of attacks, including noisy data attacks, Byzantine attacks, and label flipping attacks.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Builds on 15 of its 38 references that are in the map. First posted on arXiv 18 Jul 2021.

Builds on

15 of 38 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2019Advances and Open Problems in Federated Learningcited by 3,040
  3. 2016Deep Learning with Differential Privacycited by 2,718
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 708
  5. 2019Neural Graph Collaborative Filteringcited by 660
  6. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  7. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  8. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  9. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 326
  10. 2018Mitigating Sybils in Federated Learning Poisoningcited by 160
  11. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  12. 2018Byzantine Stochastic Gradient Descentcited by 81
3 more
  1. 2019Byzantine-Robust Federated Machine Learning through Adaptive Model Averagingcited by 70
  2. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
  3. 2017Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descentcited by 59

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Cited by year

Citing papers per year: 2023: 5; 2024: 2; 2025: 2.

9 papers.

  1. 2024Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Surveycited by 87
  2. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 18
  3. 2023FedNoisy: Federated Noisy Label Learning Benchmarkcited by 11
  4. 2024Tackling Noisy Clients in Federated Learning with End-to-end Label Correctioncited by 11
  5. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  6. 2025FNBench: Benchmarking Robust Federated Learning against Noisy Labelscited by 6
  7. 2023Trustworthy Federated Learning: A Surveycited by 4
  8. 2025Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Modelscited by 1
  9. 2023Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning (full version)cited by 0