Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML

• URL: http://arxiv.org/abs/2501.09621v1
• Date: Thu, 16 Jan 2025 16:00:52 GMT
• Title: Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML
• Authors: Tehila Dahan, Kfir Y. Levy,
• Abstract summary: Asynchronous systems struggle with maintaining integrity against Byzantine failures. We introduce a novel weighted robust aggregation framework to tackle these issues. We achieve an optimal convergence rate for the first time in an asynchronous Byzantine environment.
• Score: 8.419845742978985
• License:
• Abstract: We address the challenges of Byzantine-robust training in asynchronous distributed machine learning systems, aiming to enhance efficiency amid massive parallelization and heterogeneous computing resources. Asynchronous systems, marked by independently operating workers and intermittent updates, uniquely struggle with maintaining integrity against Byzantine failures, which encompass malicious or erroneous actions that disrupt learning. The inherent delays in such settings not only introduce additional bias to the system but also obscure the disruptions caused by Byzantine faults. To tackle these issues, we adapt the Byzantine framework to asynchronous dynamics by introducing a novel weighted robust aggregation framework. This allows for the extension of robust aggregators and a recent meta-aggregator to their weighted versions, mitigating the effects of delayed updates. By further incorporating a recent variance-reduction technique, we achieve an optimal convergence rate for the first time in an asynchronous Byzantine environment. Our methodology is rigorously validated through empirical and theoretical analysis, demonstrating its effectiveness in enhancing fault tolerance and optimizing performance in asynchronous ML systems.

Related papers

• Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency [0.9999629695552195]
  We use gradient modeling to better understand the impact of design choices in asynchronous FL algorithms. We characterize in particular a fundamental trade-off for optimizing asynchronous FL. We show that these optimizations enhance accuracy by 10% to 30%.
  arXiv  Detail & Related papers  (2025-02-12T08:38:13Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• FADAS: Towards Federated Adaptive Asynchronous Optimization [56.09666452175333]
  Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.
  arXiv  Detail & Related papers  (2024-07-25T20:02:57Z)
• FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting [9.261784956541641]
  Asynchronous Federated Learning (AFL) methods have emerged as promising alternatives to their synchronous counterparts by the slowest agent. AFL model training heavily towards agents who can produce updates faster, leaving slower agents behind. We introduce FedStaleWeight, an algorithm addressing in aggregating asynchronous client updates by employing average staleness to compute fair re-weightings.
  arXiv  Detail & Related papers  (2024-06-05T02:52:22Z)
• Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training [8.419845742978985]
  We investigate the challenging framework of Byzantine-robust training in distributed machine learning (ML) systems. Our first contribution is the introduction of an efficient meta-aggregator that upgrades baseline aggregators to optimal performance levels. Our paper highlights its theoretical and practical advantages for Byzantine-robust training, especially in simplifying the tuning process.
  arXiv  Detail & Related papers  (2024-05-23T16:29:30Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• On the Role of Server Momentum in Federated Learning [85.54616432098706]
  We propose a general framework for server momentum, that (a) covers a large class of momentum schemes that are unexplored in federated learning (FL) We provide rigorous convergence analysis for the proposed framework.
  arXiv  Detail & Related papers  (2023-12-19T23:56:49Z)
• Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory [5.502596101979607]
  We propose a novel asynchronous FL framework that integrates an incentive mechanism based on contract theory. Our framework exhibits a 1.35% accuracy improvement over the ideal Local SGD under attacks.
  arXiv  Detail & Related papers  (2023-10-10T09:17:17Z)
• Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
  The trade-off between robustness and accuracy has been widely studied in the adversarial literature. We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
  arXiv  Detail & Related papers  (2022-02-21T10:36:09Z)
• From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization [13.755421424240048]
  We propose a new algorithm textttFedaGrac, which calibrates the local direction to a predictive global orientation. We theoretically prove that textttFedaGrac holds an improved order of convergence rate than the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-12-17T07:26:31Z)
• 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)

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.