mlsanityData to 14 Oct 2026

FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for Byzantine Tolerant Federated Learning

arXiv 2411.03861 ↗First posted 6 Nov 2024cs.CV cs.CRcited by 0

Not on the map. Why?

Abstract (arXiv)

One of the most common defense strategies against Byzantine clients in federated learning (FL) is to employ a robust aggregator mechanism that makes the training more resilient. While many existing Byzantine robust aggregators provide theoretical convergence guarantees and are empirically effective against certain categories of attacks, we observe that certain high-strength attacks can subvert the robust aggregator and collapse the training. To overcome this limitation, we propose a method called FedSECA for robust Sign Election and Coordinate-wise Aggregation of gradients in FL that is less susceptible to malicious updates by an omniscient attacker. The proposed method has two main components. The Concordance Ratio Induced Sign Election(CRISE) module determines the consensus direction (elected sign) for each individual parameter gradient through a weighted voting strategy. The client weights are assigned based on a novel metric called concordance ratio, which quantifies the degree of sign agreement between the client gradient updates. Based on the elected sign, a Robust Coordinate-wise Aggregation(RoCA) strategy is employed, where variance-reduced sparse gradients are aggregated only if they are in alignment with the corresponding elected sign. We compare our proposed FedSECA method against 10 robust aggregators under 7 Byzantine attacks on 3 datasets and architectures. The results show that existing robust aggregators fail for at least some attacks, while FedSECA exhibits better robustness. Code - https://github.com/JosephGeoBenjamin/FedSECA-ByzantineTolerance

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 43 references that are in the map. First posted on arXiv 6 Nov 2024.

Builds on

31 of 43 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  6. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 712
  7. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  8. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  9. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  10. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  11. 2019Robust Aggregation for Federated Learningcited by 317
  12. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
19 more
  1. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  2. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  3. 2020Learning from History for Byzantine Robust Optimizationcited by 137
  4. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 128
  5. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  6. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  7. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 65
  8. 2019DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregationcited by 44
  9. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 41
  10. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  11. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  12. 2023An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learningcited by 32
  13. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 30
  14. 2019Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attackscited by 20
  15. 2024Model Poisoning Attacks to Federated Learning via Multi-Round Consistencycited by 18
  16. 2023A Huber Loss Minimization Approach to Byzantine Robust Federated Learningcited by 16
  17. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 15
  18. 2022Suppressing Poisoning Attacks on Federated Learning for Medical Imagingcited by 8
  19. 2022Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learningcited by 8

Built on by

Not cited yet.