Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks

• URL: http://arxiv.org/abs/2210.16682v1
• Date: Sat, 29 Oct 2022 20:08:07 GMT
• Title: Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks
• Authors: Guanqiang Zhou and Ping Xu and Yue Wang and Zhi Tian
• Abstract summary: In distributed learning systems, issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the model could exhibit poor out-of-sample performance. On the other hand, a portion of trained nodes might be subject to byzantine attacks which could invalidate the model.
• Score: 29.34471516011148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In distributed learning systems, robustness issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to byzantine attacks which could invalidate the learning result. Existing works mostly deal with these two issues separately. In this paper, we propose a new algorithm that equips distributed learning with robustness measures against both distributional shifts and byzantine attacks. Our algorithm is built on recent advances in distributionally robust optimization as well as norm-based screening (NBS), a robust aggregation scheme against byzantine attacks. We provide convergence proofs in three cases of the learning model being nonconvex, convex, and strongly convex for the proposed algorithm, shedding light on its convergence behaviors and endurability against byzantine attacks. In particular, we deduce that any algorithm employing NBS (including ours) cannot converge when the percentage of byzantine nodes is 1/3 or higher, instead of 1/2, which is the common belief in current literature. The experimental results demonstrate the effectiveness of our algorithm against both robustness issues. To the best of our knowledge, this is the first work to address distributional shifts and byzantine attacks simultaneously.

Related papers

• Probabilistic Contrastive Learning for Long-Tailed Visual Recognition [78.70453964041718]
  Longtailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. We propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space.
  arXiv  Detail & Related papers  (2024-03-11T13:44:49Z)
• Detection and Mitigation of Byzantine Attacks in Distributed Training [24.951227624475443]
  An abnormal Byzantine behavior of the worker nodes can derail the training and compromise the quality of the inference. Recent work considers a wide range of attack models and has explored robust aggregation and/or computational redundancy to correct the distorted gradients. In this work, we consider attack models ranging from strong ones: $q$ omniscient adversaries with full knowledge of the defense protocol that can change from iteration to iteration to weak ones: $q$ randomly chosen adversaries with limited collusion abilities.
  arXiv  Detail & Related papers  (2022-08-17T05:49:52Z)
• Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation [27.518542543750367]
  This paper aims at addressing conflicting issues in federated learning: differential privacy and Byzantinerobustness. Standard mechanisms add transmitted DP, envelops entangles with robust gradient aggregation to defend against Byzantine attacks. We show that the influence of our proposed mechanisms is deperturbed with that robust model aggregation.
  arXiv  Detail & Related papers  (2022-04-29T23:37:46Z)
• Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning [12.464625883462515]
  Adversarial attacks attempt to disrupt the training, retraining and utilizing of artificial intelligence and machine learning models. We propose a novel distributed training algorithm, partial synchronous gradient descent (ParSGD), which defends adversarial attacks and/or tolerates Byzantine faults. Our results show that using ParSGD, ML models can still produce accurate predictions as if it is not being attacked nor having failures at all when almost half of the nodes are being compromised or failed.
  arXiv  Detail & Related papers  (2021-09-05T07:55:02Z)
• Secure Distributed Training at Scale [65.7538150168154]
  Training in presence of peers requires specialized distributed training algorithms with Byzantine tolerance. We propose a novel protocol for secure (Byzantine-tolerant) decentralized training that emphasizes communication efficiency.
  arXiv  Detail & Related papers  (2021-06-21T17:00:42Z)
• Learning from History for Byzantine Robust Optimization [52.68913869776858]
  Byzantine robustness has received significant attention recently given its importance for distributed learning. We show that most existing robust aggregation rules may not converge even in the absence of any Byzantine attackers.
  arXiv  Detail & Related papers  (2020-12-18T16:22:32Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing [55.012801269326594]
  In Byzantine robust distributed learning, a central server wants to train a machine learning model over data distributed across multiple workers. A fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. We propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost.
  arXiv  Detail & Related papers  (2020-06-16T17:58:53Z)
• Byzantine-resilient Decentralized Stochastic Gradient Descent [85.15773446094576]
  We present an in-depth study towards the Byzantine resilience of decentralized learning systems. We propose UBAR, a novel algorithm to enhance decentralized learning with Byzantine Fault Tolerance.
  arXiv  Detail & Related papers  (2020-02-20T05:11:04Z)

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.